tutorial windografher

Windographer is a wind data analysis program. It reads raw data files, does advanced statistical

processing of the data, produces a variety of graphs for visualizing the data, and provides tools for quality

control of the data.

Windographer allows you to open three types of raw data files: text files, NRG Systems data logger (.RWD) files, and Microsoft Excel (.xls) files. When you open one or more raw data files, Windographer

creates a Windographer document and stores a copy of the data from each file in the document.

Windographer never modifies the data in your original data files.

� To open a file, choose Open from the File menu.

� To create a new empty data set, choose New from the File menu.

� To append data to an existing data set, choose Append from the File menu.

Getting Started

To familiarize yourself with Windographer, you can open the sample file included with the software by navigating to the sample data folder in the Windographer folder, which by default is \Application

Data\Mistaya\Windographer\SampleData. Note that the Application Data folder is hidden by default. If you

cannot see the folder in Windows, try changing the Folder Options to make hidden files visible. You can also download the sample file from www.mistaya.ca/windographer/NapoleonND.zip.

The following articles will help you get started using Windographer:

� Importing raw data files

� Importing .RWD files

� Exporting graphs

� Exporting tables

� Exporting data

Features

When you open a file, Windographer displays the data in many graphs, including:

� time series graphs

� wind roses

� daily profiles

� probability distribution function

� cumulative distribution function

� scatterplots

Windographer also performs a statistical analysis on the data set and calculates such quantities as:

� turbulence intensity

� power law exponent

� surface roughness

� wind power density

� wind power class

� Weibull k

The Data menu provides access to some more advanced features, such as:

� the gap filling module

� the quality control module

� the turbulence analysis module

� the wind shear analysis module

� the extreme wind analysis module

The Tools menu provides access to some additional tools that you might find useful.

Written by: Tom Lambert Contact: [email protected]

Last modified: May 6, 2008

A raw data file is a text file, Excel file, or RWD file containing time series data that you want to analyze in Windographer. We have designed the software with wind resource data in mind, but you can use it to

analyze other types of time series data as well.

You can import a raw data file into Windographer in two ways:

� File > Open imports a raw data file into a new Windographer document.

� File > Append adds data from one or more raw data files into an existing Windographer document.

For details, please see the articles on importing raw data files and appending data.

Raw Data File Types

Windographer can open three types of raw data files:

� Text files, also called ASCII files or flat files, can contain data elements delimited by tabs, commas,

spaces, or semi-colons. Text files often have .txt or .csv extensions, but Windographer can open

text files with any extension.

� Excel files may have .xls or .xlsx extensions. To open Excel files, you must have Microsoft Excel

2000, 2002, 2003, or 2007 installed on your computer. If you open an Excel file that contains more

than one worksheet, Windographer will ask you which worksheet contains the data you want to import.

� NRG System files must have the extension .RWD. To import .RWD files, you must have the

Symphonie Data Retriever (SDR) software installed on your computer.

Format Requirements for Raw Data Files

A raw data file can contain any number of rows and columns. Each row must correspond to one time step.

Several examples appear below. These examples show text files, but similar requirements apply to Excel

files.

The example below shows a tab-delimited text file containing wind speed data at 10m, 30m, and 40m above ground, wind direction data at 30m and 40m above ground, and temperature data. When importing

this file, Windographer would automatically detect that the column names appear in line 6, the units

appear in line 7, and the numeric data begin on line 8. It would also recognize that the time step is ten minutes. Thanks to the helpful column names, it would also recognize the instrument heights for the

speed and direction data, and the fact that the last column contains temperature data. (If the column

names were less informative, you would need to specify the instruments heights and identify the last

column as the temperature column in the Configure Data Set window.) It would also read the latitude, longitude, and elevation from the file header.

Raw Data Files

When importing raw data files, Windographer attempts to identify data column types (wind speed or direction, standard deviation of wind speed or direction, temperature, pressure) by referring both to the

names and to the statistical characteristics of the data columns. The statistical analysis works well if the

raw data file contains at least a few weeks of data, but for data sets covering less than one week Windographer resorts to analyzing the column names only. Longer data sets have an additional

advantage: Windographer can more reliably recognize the date and time when the data set covers more

transitions from one day to the next and one month to the next.

Windographer also refers to the data column names to detect associations between columns. In the

example below, it would recognize that the 'WS40' data column contains the mean wind speed at 40m, and that the 'WS40_SD' data column contains the standard deviation of the wind speed at 40m. If the

columns had less informative names such as 'Column1', 'Column2', and so on, then in the Configure Data

Set window you would need to indicate both that the measurement height of the first wind speed column is 40m, and that the third column contains the associated standard deviation data.

Tip: If you have control over the format of your raw data files and you want the data import process to work as automatically as possible, you should use

informative column names so that Windographer can automatically identify

measurement heights, column types, column associations, and so on.

Date/Time Formats

Windographer accepts many different date formats. The year can appear as a two-digit or a four-digit

number. The day can be specified by day of the year or by month and day. The year, month, and day can

appear in separate columns or in a single column. When the year, month and day appear in a single column, they can be separated by dashes, periods, or forward slashes, and the year, month, and day can

be in any order. They can also be all together with no separators. In the example below, the last column

contains the date in MMDDYY format:

Windographer can also read dates that include the names or abbreviations of months, such as in the

following example

The data and time information can also be together in one column with no delimiters, as in the following

example:

The time-of-day data can be in HH, HH:MM, or HH:MM:SS format, with or without the colon delimiters.

The hours and minutes must appear in the same column, not in separate columns. By default, Windographer assumes that the time stamp in the raw data file indicates the start of the time step, but in

the Configure Data Set window you can change that to the end or the middle.

If the file does not contain date/time data or Windographer cannot recognize the data/time data, a

window will appear asking you to specify the start date and time, along with the length of time step. In this case, Windographer will assume the data set contains no gaps.

Gaps

Windographer can read data files containing any number of gaps (missing elements). It will recognize that that data elements are missing if they are blank or contain alphabetic characters. The example below

shows a file that uses blank elements to indicate gaps:

In the example below, gaps appear as '-n/a-' rather than as blanks:

Windographer will also recognize when entire time steps are simply missing from the data file, as in the

example below where all of June 29 and 30, and parts of June 28 and July 1, are missing:

Tip: Many data loggers record certain very high or very low numbers (such as -

9999) to indicate missing or erroneous measurements. You can filter these values out using the filter settings in the Configure Data Set window. You can also fill the

resulting gaps using the Fill Gaps function or the Quality Control window.

See also

Importing raw data files

Importing .RWD files

Appending data

Filling gaps

Configure Data Set window

Quality Control window

Written by: Tom Lambert

Contact: [email protected]

Last modified: April 28, 2008

You can import a raw data file into Windographer in two ways. File > Open imports a single raw data file into a new Windographer document, whereas File > Append adds data from one or many raw data files

into an existing Windographer document.

When you import a raw data file, Windographer analyzes the data and tries to determine:

� the number of rows and columns of data

� the name and units of each data column

� the start time and the length of time step

� the type of data that each data column contains (speed, direction, temperature, etc.)

� the measurement height of each wind speed and wind direction sensor

� any column associations (e.g. a standard deviation column associated with a wind speed data column)

� the latitude, longitude, and elevation

If Windographer cannot determine the state time or the time step, it will ask you to specify them. If it detects any data elements that appear to be out of chrolological order, or that do not appear to conform

to the time step, it will display the Date/Time Anomalies window to alert you to the irregularities and to

give you some control over how to handle them.

Windographer then displays the Configure Data Set window so you can verify all of the pieces of

information that it has automatically determined, and make any necessary changes. When you click OK to close the Configure Data Set window, Windographer displays the data set in the main window. If you save

the data set to a .windog file, Windographer will store the data and the configuration information in

the .windog file. When you open the .windog file in the future, you will not have to specify the configuration information, but the Configure Data Set window is always available in the Data menu if you

want to make changes.

See also

Raw data files

Appending data


Date/Time Anomalies window



Importing Raw Data Files

.RWD files are binary-format raw wind data files written by the Symphonie Data Logger software by NRG Systems. You can open .RWD files in Windographer using File > Open or File > Append, provided you

have the Symphonie Data Recorder software installed on your computer. You can download the SDR

software for free from NRG Systems website, www.nrgsystems.com.

Tip: Make sure that the SDR software is not already running on your computer

when you try importing an .RWD file into Windographer.

Note that you can open many .RWD files at once with File > Append. Hold down the Shift key or the Control key to select multiple files:

See also

Raw data files


Appending data



Importing .RWD Files

To append data from one or more raw data files to an existing Windographer document, choose Append from the File menu. You can use the append facility both to add data to the existing data columns, or to

add new data columns.

Appending Rows of Data

Windographer will add data to an existing data column if a column name in the appended data set

matches a column name in the existing data set. The diagram below illustrates a common scenario, where the appended data has the same data structure as the existing data set. Because the column names

match, Windographer simply adds data to the existing data columns:

If new data become available one day at a time or one month at a time, you can repeatedly use this

process to build up the main data set from many smaller data files. The appended time steps do not have to immediately follow the existing time steps as they do in the above diagram; they can be earlier or later

than the main data set, or even overlap with the main data set. Windographer will place the new data in

the correct chronological order according to the time stamps in the appended data set, and it will adjust the start or end date of the main data set if necessary.

Appending Columns of Data

If a column name in the appended data set does not match that of any existing column, Windographer will

add a data column to hold the new data. The diagram below illustrates a scenario where the existing data set contains one column and the appended data set contains another, so the resulting data set contains

two columns. The appended data covers a few more time steps than does the existing data set, so

Windographer lengthens the data set accordingly, and leaves the new time steps blank in the existing data column.

Appending Data

Main Data Set

Date/Time Col1 Col2

070429 05:40 8.10 287

070429 05:50 8.14 268

070429 06:00 7.53 250

070429 06:10 7.45 236

070429 06:20 6.49 232

070429 06:30 5.43 225

070429 06:40 4.81 222

070429 06:50 4.57 225

Appended Data Set

Date/Time Col1 Col2

070429 07:00 4.84 223

070429 07:10 4.65 219

070429 07:20 4.65 218

070429 07:30 4.98 217

070429 07:40 5.24 227

070429 07:50 5.62 264

append

Resulting Data Set

Date/Time Col1 Col2

070429 05:40 8.10 287

070429 05:50 8.14 268

070429 06:00 7.53 250

070429 06:10 7.45 236

070429 06:20 6.49 232

070429 06:30 5.43 225

070429 06:40 4.81 222

070429 06:50 4.57 225

070429 07:00 4.84 223

070429 07:10 4.65 219

070429 07:20 4.65 218

070429 07:30 4.98 217

070429 07:40 5.24 227

070429 07:50 5.62 264

Main Data Set

Date/Time Col1

Appended Data Set

Date/Time Col2

Resulting Data Set

Date/Time Col1 Col2

You might use this approach to aggregate data from different data loggers or even altogether different

sources.

Tip: Before appending data, Windographer displays the Configure Data Set

window so that you can verify the structure of the data set you are about to append. That is your chance to make sure the data column names are correct so

that the process works as you want it to.

Appending Overlapping Data

If a column in the appended data set has the same name as a column in the existing data set, and if it

overlaps the existing column in time, Windographer will refer to the settings in the Options window to decide how to handle the overlapping data. You can specify that the appended data always overwrites the

existing data, or that it never overwrites the existing data. A third alternative is to overwrite existing data

only where the existing data is missing. In that case, the appended data can fill gaps in the existing data, but no existing data values will be overwritten. A fourth alternative is to overwrite existing data at all

times except where the appended data is missing. This is the default setting, but you can change it in the

Tools > Options window.

Appending Multiple Raw Data Files

Note that in the Append One or More Files window, you can select multiple files by holding down the Shift key or the Control key when you click on files:

070429 05:40 8.10

070429 05:50 8.14

070429 06:00 7.53

070429 06:10 7.45

070429 06:20 6.49

070429 06:30 5.43

070429 06:40 4.81

070429 06:50 4.57

070429 07:00 4.84

070429 07:10 4.65

070429 07:20 4.65

070429 05:40 287

070429 05:50 268

070429 06:00 250

070429 06:10 236

070429 06:20 232

070429 06:30 225

070429 06:40 222

070429 06:50 225

070429 07:00 223

070429 07:10 219

070429 07:20 218

070429 07:30 217

070429 07:40 227

070429 07:50 264

append

070429 05:40 8.10 287

070429 05:50 8.14 268

070429 06:00 7.53 250

070429 06:10 7.45 236

070429 06:20 6.49 232

070429 06:30 5.43 225

070429 06:40 4.81 222

070429 06:50 4.57 225

070429 07:00 4.84 223

070429 07:10 4.65 219

070429 07:20 4.65 218

070429 07:30 217

070429 07:40 227

070429 07:50 264

When you select multiple files to append, Windographer will assume that they all share the same data structure. It will only display the Configure Data Set window once, regardless of how many files you have

selected to append.

Templates

In the Configure Data Set window, you can use templates to store data structure information to a file,

then apply it to other data files. When you append data to an existing data set, Windographer will automatically apply the main data set's template to the appended data set. If the appended data set has a

different data structure and you do not want Windographer to apply the main data set's template, you can

remove the template by choosing Remove Template:

When you remove a template, Windographer will analyze the appended data set to determine its data

structure. You can then make any necessary changes before closing the Configure Data Set window.

When you apply a template, Windographer will ask whether you want to continue to apply that template

automatically whenever appending data to the current Windographer document. If you click yes, then

Windographer will store that template and apply it, rather than the main data set template, whenever you append data to that Windographer document. Doing this can save time in a case where you repeatedly

append new data files to an existing data set as they become available, but the data structure of the new

data files does not exactly match that of the existing data set. This might occur if, for example, you

change the instrument arrangement on a meteorological tower so that it now has a different number of sensors than before, or one sensor is at at different height than before.

See also

Raw data files


Templates



Contact: [email protected] Last modified: April 29, 2007

A template is a small file encapsulating all the properties of all the data columns in a data set. You can save a template from one data set and apply it to another data set that you import into Windographer

using File > Open or File > Append. Templates can save time if you repeatedly open data files with the

same data structure, because rather than entering all the properties of each data column individually, you

can simply apply the template.

To save or apply a template, click the Template button on the Data Columns tab of the Configure Data Set window.

When you apply a template, Windographer asks you whether you want to automatically apply that

template every time you append data to that data set. If you click yes, then Windographer will store that

template and apply it each time you append to that data set. Otherwise it will automatically apply the template from the existing data set to the appended data set. If you do not want to apply a template, you

can remove the template by choosing Remove Template from the Template button.

See also


Appending data




Templates

To export data to a text file, choose Export Data from the File menu:

The Export Data window allows you to export data in one of four formats: time series, WAsP tab file,

WindSim .wws file, or WindSim .tws file. The following sections describe each of these formats. You can

export the final three formats only if your data set contains both wind speed and wind direction data.

Time Series

A time series data file contains one time step of data per line. On the time series tab, You can choose which data columns to export, and whether to export all or only a subset of the time steps in the data set.

You can also choose the data interval, which is the length of the time step, for the exported data.

Tip: If you choose a longer data interval than that of the original data set,

Windographer calculates the average of each exported data column in each of the

longer time steps. It exports vector means for wind direction data, and arithmetic means for all other types of data.

You can also indicate whether you want the export file to include the date and time for each time step, and if so you have several choices regarding format. The Time stamp indicates control lets you choose

whether the exported date and time values refer to the start or the end of the time steps.

The File delimiter control lets you specify whether to use tab characters, spaces, commas, or some other

character to separate the values in the exported file. The Missing element control lets you enter the text

string that appears in the place of any missing element or gap. By default, Windographer leaves missing

elements blank, but you may instead want to use some code like '-9999' or 'N/A' to appear in place of missing elements.

A preview of the exported file appears at the bottom of the tab. The preview

capacity does not function if the time step of the exported data differs from that of

Exporting Data

the data file. The export process will still work correctly in that case, but the

necessary calculations make the preview process impractically slow.

WAsP Tab File

A tab file is a text file with a particular format specifying the frequency distribution by wind speed and diretion sector. The file type originated with the wind flow model WAsP. The WAsP documentation gives

detailed information about the tab file format.

Windographer exports tab files that indicate frequency by direction sector and wind speed bin. If the data

set contains multiple wind speed and direction sensors, you can choose the speed and direction sensors

upon which to base the statistics. You can also choose the number of wind direction sectors and whether to base the calculations on all time steps or only on a subset of the time steps in the data set.

A preview of the tab file appears in the window. The columns correspond to the direction sectors and the

rows correspond to the wind speed bins. The frequencies are in per mille, and the frequencies in each

column sum to 1000.

WindSim WWS File

A .wws file is similar to a tab file in that it specifies the frequency histogram versus wind speed and

direction sector. But the .wws file has a specific header format and contains fractional frequency values

rather than per mille values. The wind flow model WindSim accepts data in this format. The WindSim documentation provides detailed information about the format of .wws files.

When exporting a .wws file, you can choose the wind speed and wind direction sensors on which to base

the frequency calculations, the number of direction sectors, and whether to include all time steps or just a

subset of the time steps in the data set.

WindSim TWS File

The .tws format is a second format read by the wind flow model WindSim. The .tws file contains time series data for one wind speed sensor and one wind direction sensor. If your data file contains multiple

wind speed or direction sensors, you can choose the ones to export. You can also choose to export all or

just a subset of the time steps in the data set.

See also

Exporting graphs

Exporting tables


Contact: [email protected] Last modified: October 2, 2008

From any graph in Windographer, you can export the image or the underlying data with a right-click:

Copying the image to the clipboard

To copy the image to the clipboard, choose either Copy Bitmap or Copy Metafile. Then paste the image into the application of your choice. Use the bitmap format if you plan to paste the image into a bitmap editing

application like Paint. Use the metafile format if you plan to print the image or scale it to a larger size.

Metafiles use a vector format that scales smoothly and looks better at the high resolution of a printed

document. A bitmap image will look pixelated and coarse when scaled up or printed.

Exporting the image to a file

To save the image to a file, choose Save As PNG or Save As Metafile. Portable Network Graphics (.PNG)

files use a highly compressed bitmap format that results in very small files with no distortion. Use the PNG

format if you plan to email the image or put it on a website. Virtually all browsers, graphics programs, and word processors can open .PNG files. As mentioned above, metafiles are preferable if you plan to print the

image or scale it up or down.

Tip: To avoid resizing the exported image, size the graph appropriately in

Windographer before you export the image. Almost all graphs in Windographer

size themselves according to the window in which they appear. Resize the window

to resize the graph.

Exporting the data to a file

To export the plotted data to a tab-delimited text file, choose Export Data. You can import the resulting

text file into a spreadsheet or other data processing applicaiton.

See also

Exporting data

Exporting tables

Exporting Graphs



Last modified: March 7, 2007

Windographer allows you to export the contents of any table to a text file. To do so, right-click the table and choose Export Table from the menu:

Once you export the contents of the table to a text file, you can import the text file into a spreadsheet for further analysis, or into a word processor for inclusion in a report.

See also

Exporting data

Exporting graphs



Exporting Tables

The Summary tab shows an overview of the data set. At the left Windographer lists some general properties of the data set:

The Summary tab also displays the following four graphs:

Right-click any graph in Windographer to change its properties, copy the image to the clipboard, or export

it to a file. For more information please refer to the article on exporting graphs.

Summary Tab

Variable Description

Data set properties

LatitudeNorth-south location on the globe. Windographer reads latitude from raw data files, and you can edit it in the Configure

Data Set window.

LongitudeEast-west location on the globe. Windographer reads longitude from raw data files, and you can edit it in the Configure Data Set window.

Elevation The elevation (altitude) of the site above sea level.

Start date The date and time of the beginning of the first time step in the data set.

End date The date and time of the end of the last time step in the data set.

Duration The length of time over which the data set extends.

Time step The time interval between successive recorded observations in the data file.

Calm threshold The threshold value below which Windographer interprets wind speeds as calm.

Environmental conditions

Mean temperature The average air temperature over the entire data set.

Mean pressure The average air pressure over the entire data set.

Mean air density The average air density over the entire data set.

Air density ratio The mean air density divided by standard air density of 1.225 kg/m³.

Wind power coefficients

Power density at

50mThe average wind power density at 50m above ground.

Wind power class The class into which the wind power density at 50m falls.

Wind shear coefficients

Power law exponent

The value of the exponent that makes the power law profile best fit the measured wind shear profile.

Surface roughness The value of surface roughness that makes the logarithmic law profile best fit the measured wind shear profile.

Roughness class A dimensionless number based on the surface roughness.

Roughness

descriptionA description of a terrain typified by the calculated value of surface roughness.

Graph Description

Wind Shear Profile

This graph shows the average wind speed at each height above ground, as well as the best-fit logarithmic profile and power law profile. More detail on the wind shear is available on the Wind Shear Analysis window.

Wind Rose

This polar plot shows the frequency with which the wind blows from each direction sector, or the average wind speed in each

direction sector. Click the button at the top right to modify the properties of the wind rose. More options are available on the Wind Rose tab.

Seasonal Wind Speed Profile

This graph shows the average wind speed in each month of the year for each height above ground. In data sets spanning

multiple years, the monthly averages include values from all years. See the Time Series tab and monthly statistics table for data on each individual month.

Daily Wind

Speed Profile

This graph shows the average wind speed in hour of the year for each height above ground. See the Daily Profile tab for the

average daily profile by month.

See also

Time Series tab

Wind Rose tab

Daily Profile tab

PDF tab

CDF tab

Scatterplot tab

Boxplot tab

DMap tab

Tables tab





The Time Series tab displays one or two graphs with which you can plot any of the data columns (including calculated columns) in the data set. Using the buttons at the top of the page you can choose

to display the measured data themselves, or the daily, monthly, or annual averages of those data.

(Average wind direction values refer to vector averages.) Using the scroll bar and zoom buttons at the

bottom of the page you can plot any portion of the data set.

Use the checkboxes on the right side of the window to choose which data columns you want to display. Data checked in the first column will be displayed in the upper graph. You can plot data columns with one

type of units on the left y-axis, and a second type of units on the right y-axis. The data column that is

select first will be displayed on the left. In the example below, the user has chosen to display temperature and air density, with different units shown on the left and right y-axis of the top graph. The lower graph

displays three wind speed columns. Note that Windographer has disabled the checkboxes of the first

graph corresponding to data columns with different units. To plot the direction on the top graph, the user can simply uncheck the temperature box and then select the direction.

Use the scroll bar below the graph to move the graph forward or backward in time, and click the zoom

buttons to the right of the scroll bar to zoom in or out. The third zoom button zooms out to display all the

data.

Time Series Tab

Click and drag on the graph to select an interval on which to zoom:

Right-click the graph to change its properties, copy the image to the clipboard, or export it to a file. For

more information please refer to the article on exporting graphs.

See also

Summary tab

Wind Rose tab

Daily Profile tab

PDF tab

CDF tab

Scatterplot tab

Boxplot tab

DMap tab

Tables tab

Reports tab


Contact: [email protected] Last modified: February 20, 2009

The Wind Rose tab allows you to create many types of polar plots related to the wind direction. This page appears only if you have identified at least one wind direction data column on the Configure Data Set

window.

On the left side of the Wind Rose tab are several controls that allow you to specify the type and properties

of the wind rose(s) that Windographer creates. The radio buttons at the top allow you to choose between

the four main types of wind roses. If you choose Frequency by direction, Windographer will create a wind rose showing the frequency with which the wind direction falls within each direction sector. If the data set

contains data from two or more wind direction sensors, you can use the drop-down box labeled Direction sensor to choose the wind direction sensor on which you would like Windographer to base the wind

frequency rose. The example below shows that the wind blows most often from the WSW direction:

In wind frequency roses, the radius indicates frequency. In the above example, the frequency with which

the wind blows from the WSW direction (at a wind speed above the calm threshold) is about 7%. The least frequent wind direction (about 1%) is the N direction. At the top right of each wind frequency rose

Windographer indicates the calm frequency, or the frequency with which the wind speed is equal to or less

than the calm threshold. In the example above the calm frequency is 6%. Windographer does not include these calm winds in the wind rose diagram. To determining whether the wind speed in a particular time

step is sufficiently high to include that time step's wind direction value in a wind rose, Windographer

refers to the wind speed sensor that is closest in height to the chosen wind direction sensor.

If you choose Mean value by direction Windographer will create a different kind of wind rose showing the

mean (average) value of a particular data column wind direction. The example below plots the average wind speed at 50m versus wind direction. The wind rose indicates that that winds from the WSW direction

tend to be the strongest, with an average wind speed of over 10 m/s. Winds from the NE direction tend to

be the lightest, averaging less than 4 m/s.

Wind Rose Tab

You can plot any data column you wish with this kind of wind rose. Use the drop-down box labeled Data column to choose the data column you wish to plot. And as with wind frequency roses, use the drop-down

box labeled Direction sensor to choose a wind direction sensor. The example below plots the average solar

radiation versus wind direction. This diagram shows that the conditions tend to be very sunny when winds blow from north and east, and very cloudy when winds blow from the south and west. The average solar

radiation is near zero when the wind is blowing from the NW.

The type of wind rose Windographer creates when you choose Total value by direction is exclusively for

plotting the total amount of wind energy coming from each direction sector. The example below shows

that about 45% of the total amount of energy in the wind comes from the WSW direction, with another

20% coming from the W direction, and another 16% coming from the SW direction.

If you choose Scatterplot Windographer will create another kind of polar plot, drawing a small X mark for

every data point. As with the plots of mean value by direction, you use the drop-down boxes to select the

data column and direction sensor. The example below plots the wind speed at 50m versus the wind

direction. In this example the strongest winds all blow from the west.

With the Year and Month drop-down boxes, you can choose to plot data for a specific year and/or a

specific month. If you select All from both drop-down boxes, Windographer will plot all the data from the entire data set. If you select all years and one particular month, Windographer will plot data from that

month for all years in the data set. For example, if you choose April in a four-year data set, the graph will

include data from all four Aprils. If you select one particular year and all months, Windographer will plot

data from only that year. If you select a particular year and a particular month, Windographer will plot data for that single month.

Under the heading Display, if you choose Single wind rose Windographer will create a single wind rose

diagram using all data for the selected time frame. If you choose By month Windographer will create

twelve separate diagrams, one for each month of the year (this option is not applicable if you have chosen

a specific month from the Month drop-down box. If you choose By time of day Windographer will create twelve separate diagrams, one for each two-hour segment of the day. The checkbox labeled Use common scale allows you to choose whether all twelve wind roses should use the same scale to facilitate

comparison.

The remaining controls on the left side of the Wind Rose tab affect the appearance of the wind rose

diagrams. From the Drawing style radio buttons, choose the style you prefer: either Point-to-point, shown

below on the left, Pie slices, shown in the center, or Outlined pie slices, shown on the right.

The slider bar labeled Direction sectors lets you experiment with different numbers of direction sectors.

The optimal number of direction sectors depends on the resolution and character of the wind direction

data, the size of the diagram, and your own preferences.

Tip: You can set the default drawing style and number of sectors for wind rose

diagrams in Tools > Options.

Right-click any wind rose to copy the image to the clipboard or export it to a file. For more information

please see the article on exporting graphs.

See also

Summary tab

Time Series tab

Daily Profile tab

PDF tab

CDF tab

Scatterplot tab

Boxplot tab

DMap tab

Tables tab

Reports tab

Exporting graphs

Options window

Calm threshold



Last modified: January 28, 2008

The Daily Profile tab displays the average daily profile of one or more data columns. The drop-down box in the top left corner of the page lets you choose which data column to plot. If you choose Wind speeds,

Windographer plots the average daily profile of every wind speed data column in the data set. The other

data columns appear individually in the drop-down box.




include data from all four Aprils. If you select one particular year and all months, Windographer will plot

data from only that year. If you select a particular year and a particular month, Windographer will plot data for that single month.

If you select All from the Month drop-down box, you can choose to display the data in a single plot or in

twelve separate plots, one for each month of the year.

To calculate the average daily profile of a set of data points, Windographer finds the average value of all

of the points that occur within the hour of 12:00am to 1:00am, then all those that occur within the hour of 1:00am to 2:00am, and so on for each of the 24 hours of the day.

Tip: The daily profile table displays similar data in tabular form. You can create the daily profile table on the Tables tab of the main window.

Right-click any graph to change its properties, copy the image to the clipboard, or export it to a file. For

information about exporting graphs from Windographer, please refer to the article on exporting graphs.

See also

Daily Profile table

Summary tab

Time Series tab

Wind Rose tab

PDF tab

CDF tab

Daily Profile Tab

Scatterplot tab

Boxplot tab

DMap tab

Tables tab

Exporting graphs



The PDF tab displays a graph of the probability distribution function (abbreviated PDF) of a data column. The graph shows the frequency with which the variable falls within different bins. The example

below shows that the wind speed falls within the range of 0 to 1 m/s about 4% of the time,1 to 2 m/s

about 6.6% of the time, 2 to 3 m/s about 11% of the time, and so on.

From the Data column drop-down box at the top of the PDF tab, choose the data column you would like to

plot.

Filtering by direction

With the Wind direction sensor and Wind direction sector drop-down boxes, you can choose to create the

PDF for only those time steps when the wind blows in a certain direction. The Sectors drop-down box

allows you to experiment with different numbers of direction sectors. If you select All from the Wind

Direction sector box, Windographer will not apply any wind direction filtering. In the filter example below, the only data included in the PDF graph is when wind direction at the 10m sensor is between 75 and 105

degrees.

Filtering by date

With the Year and Month drop-down boxes, you can choose to plot data for a specific year and/or a specific month. If you select All from both drop-down boxes, Windographer will plot all the data from the

entire data set. If you select all years and one particular month, Windographer will plot data from that


include data from all four Aprils. If you select one particular year and all months, Windographer will plot data from only that year. If you select a particular year and a particular month, Windographer will plot

data for that single month.

PDF Tab

Other PDF Features

Windographer automatically chooses a reasonable bin size, but you use the Bin size input box to specify

another value.

Use the radio buttons to choose whether to create a single PDF graph for the entire data column, or

twelve different graphs showing the data separately for each month of the year. If you create a PDF of a wind speed column, Windographer also plots the best-fit Weibull distribution so you can see how well it

fits the actual distribution of wind speeds.

Tip: You can see similar data in tabular format by creating a Frequency by Bin

table or a Frequency by Bin and Direction on the Tables tab.



See also

probability distribution function

Weibull distribution

Frequency by Bin table

Frequency by Bin and Direction table

Summary tab

Time Series tab

Wind Rose tab

Daily Profile tab

CDF tab

Scatterplot tab

Boxplot tab

DMap tab

Tables tab

Reports tab

Exporting graphs

The CDF tab displays the cumulative distribution function (abbreviated CDF) of one or more data columns. The cumulative distribution function represents the probability that a variable is less than or

equal to a particular value. The example below shows that the wind speed is below 20 km/hr about 32%

of the time, and below 40 km/hr about 85% of the time.




include data from all four Aprils. If you select one particular year and all months, Windographer will plot data from only that year. If you select a particular year and a particular month, Windographer will plot

data for that single month.



See also

Summary tab

CDF Tab

Time Series tab

Wind Rose tab

Daily Profile tab

PDF tab

Scatterplot tab

Boxplot tab

DMap tab

Tables tab

Exporting graphs


Contact: [email protected] Last modified: May 15, 2006

The Scatterplot tab allows you to plot one data column versus another. Choose the data column to plot on the y-axis from the Plot drop-down box, and the data column to plot on the x-axis from the versus drop-

down box. The scatterplot will display data for all time steps that contain valid data in both the x-axis

column and the y-axis column, and that satisfy any filter conditions that you impose.

Filtering by Wind Direction

With the drop-down boxes labeled Wind direction sensor and Wind direction sector, you can create a scatterplot for only those time steps when the wind blows in a certain direction. The Sectors drop-down

box allows you to experiment with different numbers of direction sectors. If you select All from the Wind

Direction sector box, Windographer will not apply any wind direction filtering. Note that when you filter by

direction sector, the scatterplot will exclude any time step that does not contain valid data in the wind direction sensor on which you have chosen to base the directional filtering.

To display only data corresponding to a west wind, for example, you could use the following filter settings.

The scatterplot would therefore only include time steps in which the 10m wind direction sensor reports a

value between 255° and 285°.

Filtering by Date

With the drop-down boxes labeled Year and Month, you can choose to plot data for a specific year and/or a

specific month. If you select All from both drop-down boxes, Windographer will not filter by month or

year. If you select all years and one particular month, Windographer will plot data from that month for all

years in the data set. For example, if you choose April in a four-year data set, the graph will include data from the month of April for all four years. If you select one particular year and all months, Windographer

will plot data from all months in only that year. If you select a particular year and a particular month,

Windographer will plot data for that single month.

The example below shows the wind speed at 45m above ground versus the wind speed at 10m above

ground in December. Normally the 45m wind speed should almost always exceed the 10m wind speed, but this scatterplot shows that the opposite occurred in many time steps. (These points appear highlighted

in red.) Another thing that does not look right is that in several time steps one anemometer is recording

near-zero wind speeds while the other is recording moderate wind speeds, between 3 m/s and 12 m/s. (These points appear highlighted in yellow.) Both of these features of the scatterplot seem to indicate a

problem with the data set. Below we will investigate further using another capability of the the Scatterplot

tab, the ability to filter by a data column.

Scatterplot Tab

Filtering by Data Column

For some scatterplots, you may want to filter the data according to the value of a particular data column. You can do that by clicking the checkbox labeled Data column, choosing the data column from the drop-

down box, and then specifying the minimum and/or maximum values for that data column. (This is an

inclusive range, meaning Windographer will include data greater than or equal to the minimum value, and less than or equal to the maximum value.)

For example, if you suspected that the errant points in the above scatterplot were due to the effects of

anemometer icing, you might want to filter those data according to temperature. To display data only for

times when the temperature was above freezing, you could use the following filter settings:

When we apply that filter to show only above-freezing data, all of the extraordinary data points disappear

from the scatterplot, as shown below. This is evidence that icing is indeed to blame for the apparent

problems in the December data. With the Quality Control window you could investigate further and even

remove problem segments from the data set.

Line of best fit

The checkbox allows you to select whether you want the line of best fit to appear on the graph. Windographer calculates the line of best fit by performing a linear least squares regression. To the right of

the checkbox, Windographer displays the equation of the line of best fit, along with the r2 value which

indicates the goodness of fit. The r2 value is a number between 0 and 1. The closer it is to one, the better the fit. When the line of best fit is shown, Windographer also allows you to force the zero intercept.



See also

Summary tab

Time Series tab

Wind Rose tab

Daily Profile tab

PDF tab

CDF tab

Boxplot tab

DMap tab

Tables tab

Reports tab


Exporting graphs


Last modified: February 24, 2009

The Boxplot tab graphically displays, for each month of the year and for the overall data set, the following five statistical measures:

� the mean

� the average daily high and average daily low

� the maximum and minimum values



See also

Summary tab

Time Series tab

Wind Rose tab

Daily Profile tab

PDF tab

CDF tab

Scatterplot tab

DMap tab

Tables tab

Exporting graphs




Boxplot Tab

The DMap tab plots a data column in a matrix format, with day of the year on the x-axis and hour of the day on the y-axis. Each time step of the year is therefore represented by a tiny rectangular area of the

graph. The colour of that rectangular area indicates the value of the variable in that time step. This format

is often useful for identifying daily and seasonal patterns in the data. The example below shows the hourly

solar radation over one year. Several features of this solar resource are evident in this graph: the sun shines during the day and not at night, summer days are longer than winter days, the maximum intensity

of solar radiation is higher in the summer than in the winter, afternoons tend to be cloudier than mornings

during the summer, and entire days with very low solar radiation can happen at any time of the year.

DMap is short for 'data map'.

Right-click a DMap to change its properties, copy the image to the clipboard, or export it to a file. For


See also

Summary tab

Time Series tab

Wind Rose tab

Daily Profile tab

PDF tab

CDF tab

Scatterplot tab

Boxplot tab

Tables tab

DMap Tab

Exporting graphs




The Data Set Summary table is one of the tables you can create on the Tables tab of Windographer's main window. It contains the following information.

The same information appears on the Summary tab.

Right click on this or any table to export it to a text file or to copy it to the clipboard:

See also

Summary tab

Tables tab




Data Set Summary Table


Latitude You enter the latitude on the Data Set tab of the Configure Data Set window.

Longitude You enter the longitude on the Data Set tab of the Configure Data Set window.

Elevation Altitude above sea level. You enter the elevation on the Data Set tab of the Configure Data Set window.

Start date The time of the beginning of the first time step.

End date The time of the end of the last time step.

Duration The time interval between the start date and the end date.

Length of time step The time interval between the start and end of each time step.

Calm threshold The threshold wind speed for inclusion in a wind rose.

Mean temperature The mean air temperature over the entire data set.

Mean pressure The mean air pressure over the entire data set.

Mean air density The mean air density over the entire data set.

Power density at 50m The mean wind power density at 50m above ground.

Wind power class The class into which falls the wind power density at 50m.

Power law exponent A measure of the overall wind shear.

Surface roughness A measure of the overall wind shear.

Roughness class The class into which falls the surface roughness.

Roughness class The type of terrain typified by a similar value of surface roughness.

The Wind Speed Sensor Summary table is one of the tables you can create on the Tables tab of Windographer's main window It contains the following information:


See also

Tables tab




Wind Speed Sensor Summary Table


Height above ground You specify the height of each wind speed sensor on the Configure Data Set window.

Mean wind speed The mean (average) value of the wind speeds at this height.

Median wind speed The median value, below which 50% of the values fall.

Min wind speed The lowest recorded value of wind speed at this height.

Max wind speed The highest recorded value of wind speed at this height.

Mean power density The mean wind power density at this height.

Mean energy content The mean wind energy content at this height.

Energy pattern factor A factor related to the distribution of wind speeds.

Weibull k The Weibull shape factor at this height.

Weibull c The Weibull scale factor at this height.

1-hr autocorrelation coefficient

A measure of the autocorrelation of the wind speeds at this height.

Diurnal pattern

strengthA measure of how strongly the wind speed depends on the time of day at this height.

Hour of peak wind speed

The hour of the day with the highest average wind speed at this height.

Mean turbulence intensity The mean value of the turbulence intensity at this height. See the Turbulence Analysis window for more details.

Standard deviation The standard deviation of the wind speed values at this height.

Coefficient of variation The standard deviation divided by the mean wind speed times 100%.

Frequency of calms The frequency with which the wind speed is at or below the calm threshold.

Possible records The total number of time steps in this data column.

Valid records The number of time steps that contain data for this data column.

Missing recordsThe number of time steps that do not contain data for this data column. A data value may be missing because it was missing in the original raw data file, or because it was filtered out or removed in the Quality Control window.

Data recovery rate The ratio of valid records to possible records.

This table shows for each year the number of possible records, number of valid records, data recovery rate, mean, median, min, max, and standard deviation of a data column. For wind speed data columns, it

also shows the best-fit Weibull parameters.

Tip: Mean wind directions refer to vector means.


You can create this table and many others on the Tables tab of Windographer's main window.

See also

Data recovery rate

Weibull k parameter

Weibull c parameter

Vector mean

Tables tab

Monthly Statistics table

Directional Statistics table



Annual Statistics Table

This table shows for each month the number of possible records, number of valid records, data recovery rate, mean, median, min, max, and standard deviation of a data column. For wind speed data columns, it

also shows the best-fit Weibull parameters. It also shows the mean for all data and the mean of monthly

means.

Tip: Mean wind directions refer to vector means.

If you check the checkbox labeled Combine years together, the table will contain 13 rows, one for each month of the year and one for the entire data set. In that case, the January row will represent all January

data in the data set, regardless of year. If you do not check that checkbox, the table will contain as many

rows as there are months in the data set, plus one for the entire data set. In that case, Windographer calculates and displays the January 2004 statistics separately from the January 2005 statistics.



See also

Data recovery rate

Vector mean

Mean of monthly means

Weibull k parameter

Weibull c parameter

Tables tab

Annual Statistics table





Monthly Statistics Table

This table shows the number of possible records, number of valid records, mean, median, min, max, and standard deviation of a data column for each wind direction sector. For wind speed data columns, it also

shows the best-fit Weibull parameters. Use the controls on the left to select a data column, to filter the

data by year and/or month, and to choose the number of direction sectors.

Note: The number of possible records refers to the number of time steps in which

the wind direction sensor reports data. (Any time steps in which the wind direction sensor is missing data will not be included in the number of possible records.) The

number of valid records is the number of those possible time steps in which the

selected data column also reports data.



See also

Weibull k parameter

Weibull c parameter

Tables tab





Directional Statistics Table

This table shows the mean value of a data column for each hour of the day and each month of the year. You can filter for a particular year using the drop-down box on the left hand side of the tab.

Tip: You can create daily profile graphs on the Daily Profile tab of the main window.



See also

Daily Profile tab

Tables tab

Seasonal Profile table




Daily Profile Table

This table shows the mean value of a data column for each month of the year and each year of the data set. A cell appears blank if the selected data column contains no data for that year and month.



See also

Tables tab

Daily Profile table



Seasonal Profile Table

This table shows the number of occurrences and the frequency with which the values in a particular data column fall within various bins. You can set the bin size and filter by year, month, and direction sector

using the controls on the left hand side of the tab.

Tip: You can create frequency histogram graphs on the PDF tab of the main

window.



See also

PDF tab

Tables tab

Statistics by Bin table




Frequency by Bin Table

In the Statistics by Bin table, Windographer shows the statistical characteristics of one data column versus bins defined by

a second data column. The example below shows the statistical characteristics of the 'Power Law Exponent' data column versus bins defined by the 'Speed 45m' data column.

The highlighted row shows that the data set contains 2,985 time steps in which the 'Speed 45m' column falls between 12 m/s and 14 m/s, and that within those 2,985 time steps, the 'Power Law Exponent' column has a mean value of 0.108, a

median value of 0.078, a minimum value of -0.143, a maximum value of 1.905, and a standard deviation of 0.10.

You can adjust the bin size and filter for year, month, and direction sector using the controls on the left hand side of the

tab.



See also

PDF tab

Tables tab

Frequency by Bin table




Statistics by Bin Table

The Summary Report displays the statistical characteristics of the data set that wind energy analysts typically consider most important.

Data Set Properties

This section of the report provides an overview of the data set. The information shown in the properties

table also appears on the Summary tab, and in the Data Set Summary table on the Tables tab. The monthly statistics for temperature appear as a boxplot. This graph also appears on the Boxplot tab.

Wind Speed and Direction

This section of the report includes two graphs from the Summary tab, one from the PDF tab, and three

wind roses from the Wind Rose tab. Please refer to the articles on those windows for further information

about any particular graph. Each graph in this section displays data from the uppermost wind speed sensor and/or the uppermost wind direction sensor.

Wind Shear

This section of the report displays four graphs from the Wind Shear Analysis window. For further

information, please refer to the help file article on that window. In these graphs, Windographer displays either the power law exponent or the surface roughness, depending on the preference you have specified

in the Options window.

Turbulence Intensity

This section of the report includes four graphs from the Turbulence Analysis window. Each graph displays data from the uppermost wind speed sensor that has an associated standard deviation data column. The

graphs display either the characteristic turbulence intensity or the representative turbulence intensity,

depending on which IEC standard you have chosen as your preference on the Options window.

Data Column Properties

This section of the report contains a table showing the label, units, and measurement height, of each data column, as well as the number of records and some basic statistics (mean, minimum, maximum, standard

deviation). This table also appears on the Tables tab as the Data Columns table.

See also

Uppermost wind speed sensor

Summary tab

Wind Rose tab

Boxplot tab

Tables tab

Reports tab

Summary Report

Wind Shear Analysis window

Turbulence Analysis window

Options window

Written by: Linda Sloka



The Monthly Report displays detailed data for any calendar month that the data set covers.

Report Settings

This section of the report shows the name of the data set, the month covered, and the time the report

was generated.

Wind Speed Data

This graph shows the wind speed through the specified month for all wind speed sensors.

Wind Direction Data

This graph shows the wind direction through the specified month for all wind direction sensors.

Temperature Data

This graph shows the temperature through the specified month.

Tip: You can create each of these time series graphs on the Time Series tab.

See also

Reports tab

Time Series tab




Monthly Report

In the Configure Data Set window you specify the properties of each data column and the properties of the entire data set. This window appears when you import a raw data file or when you choose Configure

Data Set from the Data menu or when you select it from the toolbar:

This window consists of two tabs. The Data Columns tab lists the properties of each data column, while the Data Set tab gives access to the properties of the entire data set.

Data Columns Tab

On this tab, each data column appears as an item in the list control. Click an item to select a data column,

then use the controls in the Data column properties section of the window to specify the type, label, units,

and color of that data column. The data column type identifies whether it contains wind speed, wind direction, temperature, pressure, or some other type of data. Set the type to Hidden if you do not want

the data column to appear in any graph or in any list of data columns.

You can identify any number of wind speed and wind direction columns, but only one temperature column

and one pressure column. Windographer uses the temperature and pressure data to calculate air density.

Some data loggers record very high or very low numbers to indicate errors or missing data elements. Click Apply filter to filter out these and other extreme values. Windographer will treat any values outside the

acceptable range as gaps or missing elements. If you want to, you can fill the resulting gaps with

Windographer's gap filling module or in the Quality Control window.

When you select a wind speed data column, the Associated data columns section appears. From the Std. dev. drop-down box, choose which data column, if any, contains the standard deviation of the wind speed within each time step. Windographer uses this to calculate turbulence intensity. From the Gust drop-down

box, choose which data column, if any, contains the peak wind gust within each time step. Windographer

uses this in the Extreme Wind Analysis window.

Tip: You can save the properties for all data columns to a template file. Then in

the future if you open a raw data file with the same data structure, you can simply

Configure Data Set Window

apply the template rather than re-entering the information for each data column

individually. See the article on templates for details.

Click the Assign Default Colors button if you want Windographer to automatically assign a color to each

data column. To wind speed data columns, Windographer assigns a sequence of blue colors ranging from

dark blue for the highest wind speed sensor to light blue for the lowest. Similarly, it assigns an orange color sequence to the wind direction columns and a blue-green color sequence to the wind speed standard

deviation columns. Temperature and pressure columns also receive specific colors.

You can select more than one data column at a time using by using SHIFT and click for adjacent data, or

CRTL and click for nonadjacent data. This could be used to change the units of many columns at once, or

to apply the same filter to all the wind speed columns. Note that some data column properties cannot be specified when more than one column is selected (for example, color) and will not be available when

multiple data columns are selected.

Data Set Tab

On this tab, you can enter a text description of the data set, specify the latitude, longitude, elevation, and calm threshold, and indicate whether the time values in the data file refer to the start, middle, or end of

the time steps.

See also


Appending data

Templates

Air density

Filling gaps


Turbulence intensity

Extreme Wind Analysis window

Elevation

Calm threshold

Probability distribution function




This window displays a record of all the modifications made to the current data set. Events that may appear in the list include:

� the importing of a data file

� a change in the configuration of the data set

� the addition of a data column such as a virtual anemometer column or a wind turbine output

column

� the filling of gaps in the data set

� the removal or replacement of a data segment

You can type and store text in the Notes box.

Tip: the Quality Control window also lists all of the data removal or

replacement events. It provides additional details on these events, and gives you

a chance to restore the original data.




Document History Window

This window lets you modify the contents of one or more data columns, for example to convert units or to apply an offset to a wind vane. Choose the data column(s) you want to modify, the time interval, the

multiplier value and the offset value. When you click OK, the window will close and Windographer will

modify the columns you have chosen.

Note: Windographer keeps a record of any modifications you make to the data

set using this window. The record appears in the Document History window.

See also

Document History window

Delete Data window


Last modified: September 16, 2008

Modify Data Columns Window

This window lets you shift an entire data set in time, or a portion of your data. To access this window, choose Time Shift from the Data menu:

Shift the entire data in time

If you choose to Shift the entire data in time, Windographer shows you the date and time of the first data

item in the data set. Enter the new start time and click OK to shift your data. Shifting the data set in this way will not affect the data itself; it will only affect the time stamps.

You could use this time shift capability to change the time zone of the data, or correct a problem related

to daylight savings time or the leap year. Or you could use it to shift the time stamps to a standard

pattern, as in the example shown below. In this example, the original data set's time stamps are offset by

three minutes from the top of the hour. So the time stamps have the pattern :03, :13, :23, and so on. The user wants to shift these time stamps so that they fall on the hour, ten minutes after the hour, twenty

minutes after the hour, and so on. She may need to do this to append this data set to another data set

that has conventional time stamps, for example.

When the user clicks OK, all time stamps in her data set will shift by three minutes, like so:

Time Shift Window

Date/Time Spd Dir

3/31/2007 21:03 9.34 180

3/31/2007 21:13 9.96 203

3/31/2007 21:23 10.86 203

3/31/2007 21:33 10.95 203

Date/Time Spd Dir

3/31/2007 21:00 9.34 180

3/31/2007 21:10 9.96 203

3/31/2007 21:20 10.86 203

3/31/2007 21:30 10.95 203

These changes become permanent when she saves the .windog file.

Shift a particular data segment in time

If you choose to shift a portion of the data in time, Windographer will show you a list of all the data columns in your data set. Select the data columns you wish to shift. In the screen shot below, data from

the Dir column will be shifted in time, but data in the Spd and Temp columns will not.

Next, specify the time interval of the data to be shifted. It could be the entire column of data, or only a

portion. In the example below, just one hour of data from May 2007 will be shifted in time.

Specify how many time steps to shift the data. In the example below, a shift of 2 time steps will move the

data 20 minutes because the data has 10 minute time steps. Next, specify if the data should be shifted forward or backward in time.

When the user clicks OK, that one-hour segment of the Dir column will be shifted two times steps, like so:

3/31/2007 21:43 10.72 225

3/31/2007 21:53 11.35 225

3/31/2007 22:03 12.29 203

3/31/2007 22:13 12.20 180

3/31/2007 22:23 10.59 225

3/31/2007 22:33 10.90 270

3/31/2007 22:43 11.39 248

3/31/2007 22:53 12.07 315

3/31/2007 23:03 10.63 203

shift

3/31/2007 21:40 10.72 225

3/31/2007 21:50 11.35 225

3/31/2007 22:00 12.29 203

3/31/2007 22:10 12.20 180

3/31/2007 22:20 10.59 225

3/31/2007 22:30 10.90 270

3/31/2007 22:40 11.39 248

3/31/2007 22:50 12.07 315

3/31/2007 23:00 10.63 203

Date/Time Spd Dir

5/30/2007 23:10 9.34 180

5/30/2007 23:20 9.96 203

5/30/2007 23:30 10.86 203

5/30/2007 23:40 10.95 203

5/30/2007 23:50 10.72 225

5/31/2007 00:00 11.35 225

5/31/2007 00:10 12.29 203

5/31/2007 00:20 12.20 180

5/31/2007 00:30 10.59 225

5/31/2007 00:40 10.90 270

shift

Date/Time Spd Dir

5/30/2007 23:10 9.34 180

5/30/2007 23:20 9.96 203

5/30/2007 23:30 10.86 203

5/30/2007 23:40 10.95

5/30/2007 23:50 10.72

5/31/2007 00:00 11.35 203

5/31/2007 00:10 12.29 225

5/31/2007 00:20 12.20 225

5/31/2007 00:30 10.59 203

5/31/2007 00:40 10.90 180

This could result in data being overwritten as in the example above. Be careful! These changes become

permanent once you save the .windog file.

Tip: You cannot undo the changes you make with the Time Shift window.

See also

Delete Data window

Modify Data Columns window



5/31/2007 00:50 11.39 248

5/31/2007 01:00 12.07 315

5/31/2007 01:10 10.63 203

5/31/2007 01:20 10.90 180

5/31/2007 00:50 11.39 225

5/31/2007 01:00 12.07 315

5/31/2007 01:10 10.63 203

5/31/2007 01:20 10.90 180

This window lets you remove columns from the data set, or delete data within a certain date range. If you choose to delete a column, Windographer will remove that column from the data set entirely, and it will no

longer appear in any list of data columns. The diagram below illustrates this situation:

If you choose to delete from a column the data within a certain data range, Windographer will remove

those data elements but the column itself will remain. The diagram below illustrates this situation:

You can use this window to change the start or end date of the data set. If you delete data from all

columns starting from the first time step, the data set start time will change accordingly. The diagram

below illustrates this situation:

Delete Data Window

Date/Time Spd Dir

2004-05-30 23:10 9.34 180

2004-05-30 23:20 9.96 203

2004-05-30 23:30 10.86 203

2004-05-30 23:40 10.95 203

2004-05-30 23:50 10.72 225

2004-05-31 00:00 11.35 225

2004-05-31 00:10 12.29 203

2004-05-31 00:20 12.20 180

2004-05-31 00:30 10.59 225

2004-05-31 00:40 10.90 270

2004-05-31 00:50 11.39 248

2004-05-31 01:00 12.07 315

2004-05-31 01:10 10.63 203

2004-05-31 01:20 10.90 180

delete

Date/Time Dir

2004-05-30 23:10 180

2004-05-30 23:20 203

2004-05-30 23:30 203

2004-05-30 23:40 203

2004-05-30 23:50 225

2004-05-31 00:00 225

2004-05-31 00:10 203

2004-05-31 00:20 180

2004-05-31 00:30 225

2004-05-31 00:40 270

2004-05-31 00:50 248

2004-05-31 01:00 315

2004-05-31 01:10 203

2004-05-31 01:20 180

Date/Time Spd Dir

2004-05-30 23:10 9.34 180

2004-05-30 23:20 9.96 203

2004-05-30 23:30 10.86 203

2004-05-30 23:40 10.95 203

2004-05-30 23:50 10.72 225

2004-05-31 00:00 11.35 225

2004-05-31 00:10 12.29 203

2004-05-31 00:20 12.20 180

2004-05-31 00:30 10.59 225

2004-05-31 00:40 10.90 270

2004-05-31 00:50 11.39 248

2004-05-31 01:00 12.07 315

2004-05-31 01:10 10.63 203

2004-05-31 01:20 10.90 180

delete

Date/Time Spd Dir

2004-05-30 23:10 9.34 180

2004-05-30 23:20 9.96 203

2004-05-30 23:30 10.86 203

2004-05-30 23:40 10.95

2004-05-30 23:50 10.72

2004-05-31 00:00 11.35

2004-05-31 00:10 12.29

2004-05-31 00:20 12.20

2004-05-31 00:30 10.59

2004-05-31 00:40 10.90 270

2004-05-31 00:50 11.39 248

2004-05-31 01:00 12.07 315

2004-05-31 01:10 10.63 203

2004-05-31 01:20 10.90 180

Date/Time Spd Dir

2004-05-30 23:10 9.34 180

2004-05-30 23:20 9.96 203

2004-05-30 23:30 10.86 203

2004-05-30 23:40 10.95 203

Date/Time Spd Dir

2004-05-31 00:00 11.35 225

2004-05-31 00:10 12.29 203

Similarly, if you delete data from all columns ending at the last time step, the data set end time will

change accordingly. The diagram below illustrates this situation:

Tip: You cannot restore data that you delete with the Delete Data window. If you want to delete a segment of data but keep the option of restoring the original

data, you must use the Quality Control window instead.

See also


Hiding data columns



2004-05-30 23:50 10.72 225

2004-05-31 00:00 11.35 225

2004-05-31 00:10 12.29 203

2004-05-31 00:20 12.20 180

2004-05-31 00:30 10.59 225

2004-05-31 00:40 10.90 270

2004-05-31 00:50 11.39 248

2004-05-31 01:00 12.07 315

2004-05-31 01:10 10.63 203

2004-05-31 01:20 10.90 180

delete

2004-05-31 00:20 12.20 180

2004-05-31 00:30 10.59 225

2004-05-31 00:40 10.90 270

2004-05-31 00:50 11.39 248

2004-05-31 01:00 12.07 315

2004-05-31 01:10 10.63 203

2004-05-31 01:20 10.90 180

Date/Time Spd Dir

2004-05-30 23:10 9.34 180

2004-05-30 23:20 9.96 203

2004-05-30 23:30 10.86 203

2004-05-30 23:40 10.95 203

2004-05-30 23:50 10.72 225

2004-05-31 00:00 11.35 225

2004-05-31 00:10 12.29 203

2004-05-31 00:20 12.20 180

2004-05-31 00:30 10.59 225

2004-05-31 00:40 10.90 270

2004-05-31 00:50 11.39 248

2004-05-31 01:00 12.07 315

2004-05-31 01:10 10.63 203

2004-05-31 01:20 10.90 180

delete

Date/Time Spd Dir

2004-05-30 23:10 9.34 180

2004-05-30 23:20 9.96 203

2004-05-30 23:30 10.86 203

2004-05-30 23:40 10.95 203

2004-05-30 23:50 10.72 225

2004-05-31 00:00 11.35 225

2004-05-31 00:10 12.29 203

2004-05-31 00:20 12.20 180

Contents

� Overview

� Creating and editing search rules

� Finding and displaying data in the time series graph

� Revising data

� Restoring data

� Quality control history

� Examples

Overview

The Quality Control window allows you search the data set for abnormalities, gaps, or patterns, and, as appropriate, to delete or

replace those data. For example, you can find anomalous data that might have been caused by an icing event, and if you want, delete

the anomalous data segment or even replace it with synthesized data.

There are two ways to search for data in the Quality Control window: the first is to use the scroll bar and zoom buttons at the bottom

Quality Control Window

of the time series graph to manually navigate through the data and look for patterns in the line graphs. Another approach is to create

one or more search rules and have Windographer search the data set for you. A search rule is a set of criteria that describe the data

you are looking for. Each criterion is a comparison of a data column with either a fixed value or with another data column. Each search

rule includes a time span specification. For example, a search rule to find a potential wind vane malfunction caused by ice might

consist of the following search criteria: wind direction changes less than ten degrees and temperature is less than zero degrees Celsius

over thirty minutes.

For each search rule that you create, Windographer searches the data set for items, or segments of contiguous data, that meet the

rule's search criteria and displays a list of the items in the Search results table. You can display an item in the time series graph by

clicking its name in the table. Windographer highlights each item in the time series graph using a different color for each search rule.

In the graph, you can choose data columns to display and select segments of data to either delete or replace with synthesized data.

Creating and editing search rules

To create a new search rule, click Add to open the Create New Search Rule window.

Assign a name and color to the rule (or use the default values), and make selections in the drop down boxes that describe each search

criterion that you want to include in the rule. At the bottom of the window, define the length of time over which the rule applies. You

can specify the time interval in time steps, minutes, or hours.

You can create a rule with more than one search criterion by checking And to display a new criterion. You can assign up to four criteria

to each search rule. Remember to specify the time span for each rule that you create.

When you finish creating the rule, click OK to return to the Quality Control window. Windographer will display the new rule in the

Search rules table.

You can edit and delete search rules, and create additional rules by selecting rules in the Search rules table and using the Edit, Add,

and Delete buttons. When you create an additional search rule, you can either create the rule based on a copy of an existing rule, or

create the rule from scratch. To create a new search rule based on a copy of an existing one, in the Search rules table, click the search

rule on which to base the copy and click Add. To create the rule from scratch, make sure that none of the existing search rules is

selected in the Search rules table before clicking Add.

Finding and displaying data in the time series graph

Windographer displays a list of time steps in the Search results table that meet the search criteria for each rule. Contiguous time steps

appear as a single item in the table. When you click an item in the table, Windographer highlights and displays the item in the time

series graph. The color of the highlight matches the color shown in the Search rules table.

You can click Export to create a tab delimited text file containing the search results list.

Windographer allows you to display any data column in the time series graph. To display a data column, check one of the boxes in the

list of data column names. The two check boxes for each data column allow you to split the time series graph into two graphs: check

the leftmost box to display the data column in the top graph, check the rightmost box to display the data column in the bottom graph.

Note that the color of the lines in the time series graph is determined by the settings in the Configure Data Set window.

You can change the time scale of the graph using the zoom buttons at the bottom right corner, or simply by selecting a segment in the

graph. But to zoom to the selected segment, you must first choose Zoom to the segment under Upon selecting a segment on the graph.

For more about zooming, scrolling, changing properties of the graph, and exporting images of the graph, please see the article on the

Time Series tab.

Deleting and replacing data

A data revision is a change to a data segment. A segment is a single series of contiguous time steps in the data set. Windographer

allows three types of revisions: deletion, gap fill, and replacement. Windographer also allows you to restore original data to a deleted

or replaced segment.

� Deleting a segment removes data from columns in the data set. When you delete a segment, Windographer only removes data

from those columns that are visible in the time series graph. Deleting data leaves a gap in the data set. There is no limit to the

number of gaps that can exist in a data set: Windographer simply ignores data gaps in calculations and displays them as blank

spaces in graphs.

� Filling gaps in a segment replaces any missing data in the visible columns with synthesized data. The Quality Control window

allows you to fill gaps in individual segments of one or more data columns. See Filling Gaps for a description of how

Windographer fills gaps. If you want to fill all gaps in all columns in the data set, you can use the Gap Fill Setup window.

� Replacing a segment is equivalent to deleting a segment and then filling the resulting gap: Windographer replaces the existing

data for the columns visible in the graph with synthesized data.

Tip: When you make a data revision, Windographer keeps a permanent copy of the original data. You

can always restore the original data, even after accepting changes in the Quality Control window or in

future Windographer sessions. Windographer keeps a record of all revisions, which you can view both on

the History tab of the Quality Control window and in the Document History window.

To revise data, display the data columns that you want to revise by checking their names in the time series graph. Under Upon selecting a segment in the graph, click Select the segment for revision. To find the time steps to include in the segment, either use the

scroll bar and zoom buttons at the bottom of the graph to visually locate the time steps, or click an item in the Search results table to

jump to the item's location in the graph. In the graph, select the time steps to include in the revision: Point to one end of the segment

and drag the pointer to the right or left to select the segment. You can use the arrow buttons to set a segment's exact start and end

points.

Type a few words describing the cause for revision, or select a cause from the drop-down box. If the segment you selected includes a

search result item, Windographer will suggest the item's search rule name as the cause for revision. Click the appropriate revision

button to either delete, replace, or fill gaps in the selected segment.

In the image below, we selected the Speed 10m and Speed 30m data columns for revision. Note the segment start and end points

under Revision, indicating that Windographer will revise data in those two columns starting at 1/4/2003 09:00 and ending at 1/7/2003

10:00. Windographer will not revise the Direction 10m or Direction 45m data columns because they appear in the top graph and are

not highlighted. Windographer has suggested 'Speed sensor icing' as the cause for revision because that is the name of the search rule

that found search result item 15, which falls within the selected segment.

Clicking the button labeled Delete Segment causes Windographer to delete the segments from the two speed data columns. The deleted

segment then appears as a gap in the lower graph:

Note that Windographer will accept any number of gaps in the data set. The presence of gaps does not prevent Windographer from

performing a complete statistical analysis of the data set, nor from estimating the output of a wind turbine in the wind regime. You

can fill a gap with synthetic data using the button labeled Fill Gaps in Segment, or you can fill all gaps in the data set at once using the

Fill Gaps window.

Tip: For higher quality gap fills, remove all the problem data segments from a data column before you

fill any gaps in that data column. That way, the erroneous data segments will not corrupt the statistical

characteristics that Windographer aims to replicate in the synthetic data.

Restoring data

If you want to discard a revision and revert to the state of the data set before you opened the Quality Control window, you can click

Cancel to close the window. Windographer will discard all of your work in the window and return to the main window.

A better way to restore revisions that allows you to keep a record of the revision and restoration is to use the Restore Original Data

button on the Quality Control window's History tab. In the tab's Revision table, select the revision that you want to restore.

Windographer will highlight the segment in light grey and display the names of the affected columns in the Column table under Details.

To restore the original data, type a reason for the restoration in the Cause for restoration box, and click the button labeled Restore Original Data. A record of the restoration will appear in the revisions table, and will also be visible in the Document History window.

Quality Control history

The History tab on the Quality Control window serves two purposes: it displays a list of all data revisions and restorations that have

been made to the data set, and allows you to make restorations to revised data. You can click Export to create a tab-delimited text

file with a list of the revision history.

Tip: A similar but less detailed history of the quality control revisions appears in the Document History

window.

See also

Quality Control Examples


Fill Gaps window

Time Series tab


Tower Shading Analysis window

Written by: Paul Gilman



Measured data sets often contain gaps or missing values. Windographer can fill these gaps with synthetic data that have statistical properties similar to the measured data. To fill gaps in a data set that you have

open in Windographer, choose Fill Gaps... from the Data menu:

The Fill Gaps window will appear with some information as to the number of gaps in the data set and some settings you can choose relating to how Windographer should fill gaps in data sets containing

multiple anemometers. Once you click the Fill Gaps... button on that window, Windographer will start the

process of filling the gaps in each data column. In large data sets the gap filling process may take several

seconds.

When the gap filling process is complete, another window will appear allowing you to view the data set with the gaps filled. The synthesized data appears in the graph as thin dashed lines. In the example

below, the original data set contained no data between 10:00am on January 29 and 7:00pm on January

30. Windographer synthesized the data shown by the thin dashed line.

Windographer ensures that the synthesized data splice together well the measured values before and

after the gap, and that the statistical properties of the synthesized data (including the diurnal pattern and

the random variability from one time step to the next) closely match those of the measured data. Windographer's gap filling algorithm is based on a Markov transition matrix approach.

Filling Gaps

Windographer fills gaps in every column of a data set. The procedure works well with most types of data,

including wind speed, wind direction, temperature, and many other types of meteorological data. (It does not work as well on solar radiation data, which are better synthesized by treating the cloudiness

separately from the extraterrestrial radiation.) The example below shows wind speed and temperature

data synthesized to fill a three-day gap.

Windographer treats wind direction data in a special way, recognizing that 359° is very close to 1°. In the example below, the synthesized wind direction data makes a realistic transition from northwest (near

300°) to northeast (near 30°), passing through due north on its way. Had Windographer treated the wind

direction as a scalar variable, that transition would have likely taken more of a straight-line trajectory that

would not have passed through north.

Because it imposes the diurnal pattern on the synthesized data, Windographer can fill even lengthy gaps. The example below shows three weeks of electric load data in which Windographer has filled a five day

gap. The synthesized data display the same strong diurnal pattern as the measured data.

Data sets containing data from two or more wind speed sensors require special treatment when filling

gaps. In the graph above showing wind speed and temperature, Windographer synthesized two

independent data segments. But that approach is inappropriate when synthesizing data for multiple wind speed sensors because the data from those sensors tend to be strongly correlated. In time steps that

contain data for some but not all wind speed sensors, Windographer synthesizes the missing data by

scaling the existing data according to the wind shear profile.

In the example below, for the first half of January 18 the data set contained wind speed data at 1m, 2m, and 10m above ground, but not at 20m and 49m above ground. For those time steps Windographer

synthesized the wind speeds at 20m and 49m by scaling the 10m data to the appropriate height using the

measured wind shear profile. For the remainder of January 18, the data set contained data only for the 1m above ground, so Windographer synthesized data for all other heights based on the measured data

from 1m. For January 19, the data set contained no wind speed data, so Windographer filled the gap in

the 49m data and then synthesized data for all other heights by scaling that 49m data according to the

measured wind shear profile. The result is that all synthetic wind speed data are perfectly correlated.

See also

Fill Gaps window

Wind shear



The Virtual Anemometer window allows you to synthesize wind speed data for any height above ground. You may want to use this capability to estimate the wind speed at the hub height of a wind turbine. Most

meteorological towers are 50m or 60m in height, but many of today's largest wind turbines have hub

heights of between 70m and 120m. Windographer's virtual anemometer facility allows you to extrapolate

upwards from the heights at which you have measured wind speed data, to estimate the wind speeds at higher heights.

Tip: The procedure of extrapolating wind speeds upwards in height is inherently

uncertain, and in some cases what happens above 50m or 60m may be quite

different from what happens below. We designed Windographer's virtual

anemometer feature to do the best it can based on the measured data, but if the data measured below 50m poorly represent the wind speeds above 50m, no

algorithm will be able to accurately extrapolate. Please use this feature with

caution.

In the table at the top left of the window, enter the heights above ground for which you would like

Windographer to synthesize wind speed data. For each height you enter, you must also enter a label for

the data column that Windographer will create to store the synthesized wind speed data for that height.

Use the checkboxes in the top right to select the wind speed sensors you would like Windographer to use

in synthesizing wind speed data. By default, Windographer will use all wind speed sensors, but you can unselect any sensor you would like Windographer to ignore.

Use the radio buttons in the bottom left to select the functional form of the wind shear profile you would

like Windographer to use when synthesizing wind speed data. For details, please see the article on wind

shear.

In the bottom right of the window appear radio buttons for the two available methods for extrapolating or

interpolating to the requested heights. If you have chosen to use multiple wind sensors, Windographer will calculate the best-fit wind shear profile in every time step, and use that profile to estimate the wind speed

at the requested heights. If you have chosen to use only one wind sensor, Windographer will assume a

constant wind shear profile. In that case, you must enter either the surface roughness or the power law exponent that you want Windographer to use in extrapolating to the requested heights.

When you click the button labeled Synthesize Data & Append To Data Set, for each height you requested

Windographer will synthesize wind speed data and adds those data as a new column to your data set.

Virtual Anemometer Window

From then on, you can graph and analyze the synthesized wind speed just like you could any other data

column. Windographer will automatically assign a color to each synthesized data column, but you can make changes with the Configure Data Set window.

See also

Wind shear





The Wind Turbine Output window allows you to estimate the energy production of a wind turbine in the measured wind regime. In its calculations, Windographer takes into account the effects of varying air

density and wind shear, and it lets you enter various loss factors to account for losses due to downtime,

icing, interference from other wind turbines, and so on.

Wind Turbine Library

The drop-down box contains a list of wind turbine models. Use the buttons to the right of this drop-down

box to see details about a particular wind turbine, to add to or remove from the list, or to compare wind

turbines. The table below describes the buttons.

When you choose a wind turbine model, Windographer displays its power curve, which is a graph of power

output versus wind speed at hub height . A radio button to the right of the power curve lets you choose

from the standard hub heights of the selected wind turbine. (These standard hub heights are properties of

the wind turbine.) Choose one of the standard hub heights, or choose the last radio button and enter a custom hub height.

Losses

You can enter four separate loss terms, which Windographer combines in a multiplicative fashion into a

single number, the overall loss factor. The table below describes the four loss terms.

Wind Turbine Output Window

Button Action

DetailsOpens the Wind Turbine Details window, where you can view the detailed properties of the selected wind turbine model.

EditOpens the Edit Wind Turbine window, where you can modify

the properties of the selected wind turbine model.

NewCreates a new wind turbine model and opens the Create New Wind Turbine window, where you can specify its properties.

DeleteRemoves the currently-selected wind turbine model from the

library.

CompareOpens the Compare Wind Turbines window, where you can

compare the power curves of different wind turbines.

Loss Term Description

Downtime

losses

The energy production lost when the turbine is offline due

to scheduled maintenance or repair. Because of the high availability of modern wind turbines and because operators

can often schedule maintenance for low-wind times of the year, downtime losses rarely exceed a few percent.

Array losses

Losses resulting from aerodynamic interference between

wind turbines in a wind farm. For a single wind turbine, the array losses would be zero since no nearby wind turbines

would interfere with the airflow. For a wind farm, the array losses could range from a few percent (in the case of a

very sparse arrangement or one designed to avoid such interference) to more than 10% (in the case of a dense or

poorly-designed arrangement).

Icing/soiling losses

Losses caused by the accumulation of dirt or ice on the turbine blades, which can harm their aerodynamic

performance. Such losses could range from near zero to several percent, depending principally on the frequency of

icing events.

Outputs

When you click Calculate Output, Windographer steps through the data set to calculate the gross power

output of the wind turbine in each time step. It then calculates several monthly and annual statistics,

including the mean wind speed at hub height, time spent at zero power output, time spent at or above rated power output, average net power output, average net energy output, and average net capacity

factor. (Net means after accounting for losses.) For a full description of this process, please see the article

on calculating the energy output of a wind turbine.

Choose Monthly details to see the detailed monthly results, or choose Turbine comparision to compare the

annual statistics for many different wind turbines. Windographer adds another row to this turbine comparison table each time you click Calculate Output. Right-click the table to export the data to a text

file:

If you would like to analyze the time series data of the wind turbine power output in each time step, you can add those data to your data set using the button in the bottom right corner of the window:

When you click this button, Windographer adds to your data set a new data column containing the gross

power output of the wind turbine in each time step. (Gross means before accounting for losses.) You can then graph and analyze those data just like any other data column in Windographer. You could, for

example, generate a probability distribution function for the wind turbine power output in a particular

month, or graph the average daily profile of the turbine power output, or create a scatterplot of wind turbine output versus wind speed.

Note that if, in a single use of the Wind Turbine Output window, you calculate the output of more than one wind turbine, clicking this button will add the time series data for only the most recently-calculated wind

turbine. If you want to add data columns corresponding to two different wind turbines, you will have to

Other lossesAny other factor that might reduce energy output, including wiring losses, transformer losses, and production

losses due to cut-out at high wind speeds.

open the Wind Turbine Output window twice.

Tip: For a more accurate estimate of wind turbine output, clean up any problems

in your data set with the Quality Control window before using the Wind Turbine

Output window.



See also

Calculating the energy output of a wind turbine

Wind Turbine Details window

Create New Wind Turbine window

Edit Wind Turbine window

Compare Wind Turbines window


Exporting data



The Turbulence Analysis window allows you to observe how the turbulence in the wind varies with wind speed, wind direction, year, or month.

Display Settings

The first radio button in the top left corner of the window allows you to display turbulence versus wind

speed, wind direction, month, or time of day. The second radio button allows you to display the resulting data in graphical or tabular format.

IEC Standards

Standard 61400-1 of the International Electrotechnical Commission forms the basis of Windographer's

treatment of turbulence intensity data. The 3rd edition of that standard, released in 2005, differs in

approach from the earlier 2nd edition, released in 1999. You can choose whether you want Windographer to present results consistent with the second edition or the third edition of the standard.

The 2nd edition of IEC 61400-1 defines three turbulence categories based on the characteristic

turbulence intensity at a wind speed of 15 m/s:

The 3rd edition of IEC 61400-1 defines four turbulence categories based on mean turbulence intensity at a wind speed of 15 m/s:

If you choose the 2nd edition IEC standards, Windographer will display the mean turbulence intensity, the

characteristic turbulence intensity, and the peak turbulence intensity versus whatever you have chosen as the independent variable. If you choose the 3nd edition standards, Windographer will display

the mean turbulence intensity, the representative turbulence intensity, and the peak turbulence

intensity versus the independent variable.

If you have chosen to display turbulence versus wind speed, you can select Show IEC turbulence categories in graph to see a graph of either the characteristic turbulence intensity or the representative turbulence intensity compared to the IEC turbulence categories.

Turbulence Analysis Window

Turbulence Categories defined in IEC 61400-1 2nd Edition

CategoryCharacteristic TI

at 15 m/s

S > 0.18

A 0.16-0.18

B 0-0.16

Turbulence Categories defined in IEC 61400-1 3rd Edition

CategoryMean TI at

15 m/s

S > 0.16

A 0.14-0.16

B 0.12-0.14

C 0-0.12

Speed and Direction Sensors

In the Wind speed sensor drop-down box, Windographer lists each wind speed data column that has a

corresponding standard deviation column:

If the wind speed data column you are looking for does not appear in this drop-down box, you must return to the Configure Data Set window and associate a standard deviation column with that wind speed data

column. For more information please refer to the article on the Configure Data Set window.

Filters

In the Wind direction sector drop-down box, choose the direction sensor to which you want Windographer to refer when plotting versus direction or filtering by direction sector.

The Wind direction sector drop-down box allows you to filter for a particular wind direction sector.

Windographer disables this drop-down box when you plot turbulence versus direction. The Sectors drop-

down box lets you choose the number of direction sectors.

The Year and Month drop-down boxes allow you to filter the data for a particular year and/or a particular

month.

Check Filter by wind speed if you want to restrict the analysis only to wind speeds within a specific range.

Windographer disables this capability when you plot turbulence versus wind speed.

Calculations

To create the turbulence table, Windographer performs the following steps:

1. It searches the data set for all the time steps that meet your filter criteria. For example, if you

select the 45° - 75° wind direction sector and the month of April, it will search for time steps in

April in which the wind direction sensor that you have selected reports wind directions between 45° and 75°. For Windographer to include a time step in the analysis, both the wind speed column that

you have selected and its associated standard deviation column must report valid values.

2. It takes the resulting subset of the data and further subdivides it into bins depending on what you have chosen for the independent variable. For example, if you choose to display turbulence versus

wind speed, it subdivides the results into wind speed bins of 1 m/s width. If you choose to display

turbulence versus time of day, it subdivides the results into 24 bins, one for each hour of the day. Each bin therefore contains a set of turbulence intensity values.

3. For each bin, Windographer takes the set of turbulence intensity values and calculates its mean, standard deviation, and peak value. It then calculates either the characteristic turbulence intensity

or the representative turbulence intensity, depending on whether you have chosen the 2nd or 3rd

edition IEC standard.

The example below shows that the data set contained 856 time steps that satisfied the filter criteria, that

contained valid data in both the selected wind speed data column and its associated standard deviation data column, and in which the wind speed fell within the 14.5 - 15.5 m/s range. The resulting 856 values

of turbulence intensity exhibited a mean value of 0.076 and a standard deviation of 0.025, giving a

representative turbulence intensity of 0.076 + 1.28 * 0.025 = 0.108. The peak value from those 856

values of turbulence intensity was 0.169.

Summary Results

The summary results appear in a table in the top right corner of the window. This table repeats three

numbers that appear in the 15 m/s bin in the table of turbulence versus wind speed: the number of records in that bin, the mean turbulence intensity in the bin, and either the characteristic turbulence

intensity or the representative turbulence intensity in the bin, depending on which edition of the IEC

standard you choose. The table also reports the IEC turbulence category that results from these 15 m/s

statistics.

See also


Characteristic turbulence intensity

Representative turbulence intensity


Exporting graphs



Last modified: October 17, 2008

With this window you can see how the wind shear varies with month, time of day, wind direction and wind

speed. To access this window, choose Wind Shear from the Analyze menu or click the wind shear icon in the tool bar. For more information on the calculations, please refer to the article on wind shear.

Display Format

Choose whether you want Windographer to report the results in graphical or tabular form. Windographer

disables this radio button if you display the wind shear versus month and wind direction, or versus time of day and wind direction, as it can display these results only in tabular form.

Tip: In many cases, the tabular results report more information than do the

graphical results. Some of the tabular results report the mean wind speed for each wind speed sensor, and the number of time steps upon which the mean is based.

Wind Shear Profile

Use the radio button to choose between the logarithmic law or the power law. Windographer will report

the surface roughness if you choose the logarithmic law, or the power law exponent if you choose the

power law. Windographer disables this radio button when you view the results in a format that can display

both power law and log law results simultaneously.

Filter Settings

The filter settings allow you to confine the wind shear calculations only to a certain range of wind speeds,

for example, or only to a certain wind direction sector. Windographer may disable some of these filter

options depending on how you have chosen to display the wind shear. For example, you cannot filter by wind speed when Windographer displays the wind shear versus wind speed. Similarly, you cannot filter by

month when Windographer displays the wind shear versus month.

To confine the calculations to a certain wind speed range, check the checkbox labeled Filter by wind speed.

You will need to specify the wind speed sensor on which to base this filter. In the example below, Windographer will base its wind shear calculations only on time steps in which the Speed 45m sensor

reports wind speeds between 5 m/s and 30 m/s inclusive.

If your data set contains data from more than one wind direction sensor, Windographer will display a list

of available sensors in the drop-down box labeled Wind direction sensor. To show data for particular wind direction sector, select a range of directions from the drop-down box labeled Wind direction sector. By

Wind Shear Analysis Window

default, Windographer will display the number of direction sectors that you have specified in your

preferred wind rose settings in the Options window, but you can change the number of sectors with the drop-down box labeled Sectors.

Use the drop-down boxes labeled Year and Month to filter for a particular year and/or month.

Wind Speed Sensors

At the lower left of the window appears a list of checkboxes, one for each wind speed sensor in the data set:

Windographer will base its wind shear calculations on the wind speed sensors that you choose with these

checkboxes. You may wish to exclude a sensor if you think it is less accurate than the others, if it has a

low data recovery rate, or if you want Windographer to focus on a particular range of heights to the exclusion of others.

Tip: When determining the mean wind speed from each wind speed sensor for the purpose of calculating wind shear, Windographer considers only those time steps

that contain data from all selected wind speed sensors. If even one of the selected

wind speed sensors is missing data in a particular time step, Windographer will not use that time step to calculate the mean wind speeds.

Wind shear versus height

In this mode, Windographer displays the vertical wind shear profile, which is the mean wind speed at each

measurement height, along with the logarithmic law and power law profiles that best fit those mean wind

speeds.

Note: These mean wind speeds are based only on those time steps that contain data for all the selected wind speed sensors. They may also be filtered in one or more of the ways that this window allows.

Therefore, they may differ significantly from the overall mean wind speeds that Windographer may report

elsewhere.

Wind shear versus month

In this mode, Windographer displays the best-fit wind shear parameter for each month of the year, for the

filter settings you have specified. Note that the wind shear versus year and month mode displays the

same data broken out for individual years.

The example below shows a typical pattern for a cold climate in the northern hemisphere, where snow

cover and decreased foliage tend to reduce the surface roughness during the winter months:

Wind shear versus time of day

In this mode, Windographer displays the best-fit wind shear parameter for each hour of the day, for the

filter settings you have specified. Note that the wind shear versus month and time of day mode displays similar data broken out by month.

Wind shear versus wind direction

In this mode, Windographer displays the best-fit surface roughness or power law exponent for each

direction sector, for the filter settings you have specified. As described above, you can choose the number of direction sectors and the wind direction sensor that Windographer uses to create these results.

Wind shear versus wind speed

In this mode, Windographer displays the best-fit wind shear parameter for each wind speed bin. Note that

these wind speed bins correspond to the wind speed sensor selected in the filter section. From the tabular display, you can see the wind speed bins and number of valid data points in each one.

Wind shear versus month and time of day

In this mode, Windographer displays the best-fit wind shear parameter for each hour of the day, for each

month, for the filter settings you have specified. In graphical mode, use the checkboxes to show or hide the line for any particular month.

The example below shows data from a northern hemisphere location. The annual line shows a very typical

daily pattern, where the wind shear drops dramatically during the warm part of the day when solar

heating leads to greater vertical mixing. The colored monthly lines indicate that this daytime drop in wind

shear begins earlier and ends later in the day during the summer months. They also indicate that the summer months exhibit stronger night-time wind shear than the winter months, possibly due to increased

foliage-related roughness during the summer, or snow cover during the winter.

Wind shear versus year and month

In this mode, Windographer displays the best-fit wind shear parameter for each month of the year, for the

filter settings you have specified. In graphical mode, use the checkboxes to show or hide the line for any individual year.

Wind shear versus month and wind direction

In this mode, Windographer displays the best-fit wind shear parameter for each month of the year, for

each wind direction bin, for the filter settings you have specified. Windographer can only display this information in tabular form.

Wind shear versus time of day and wind direction

In this mode, Windographer displays the best-fit wind shear parameter for each hour of the day, for each

wind direction bin, for the filter settings you have specified. Windographer can only display this information in tabular form.



See also

Wind shear

Wind Shear Tool window

Surface roughness

Power law exponent

Exporting graphs

Wind Speed Sensor Summary report




The Tower Shading Analysis window displays graphs of differences in measurements between two wind speed sensors as a function of wind direction. Tower shading analysis requires at least two wind speed

measurements and one wind direction measurement, ideally all taken at the same height.

The controls at the top of the Tower Shading Analysis window allow you to choose the wind speed sensors

and wind direction sensors that Windographer uses to create the graphs. You can choose to display polar

or cartesian graphs of either the quotient of two speed sensors or their difference versus the wind direction. You can also choose a time period to plot, ranging from the entire data set to a single month.

The Threshold wind speed is the minimum wind speed that will appear on the graphs. Each time you open

the Tower Shading Analysis window, Windographer sets the threshold value to the Calm threshold from

the Other tab of the Configure Data Set window.

The tower shading graphs help you explore the effects of tower shading on wind speed and direction

sensors. The graphs can also help you locate icing events, although the Quality Control window has

more effective tools for that purpose. In the above example, the bumps between 90 and 120 degrees and between 180 and 210 degrees indicate possible tower shading for the year 2005. In this case, the

Tower Shading Analysis Window

threshold wind speed of 5 metres per second corresponds to the minimum average wind speed for this

data set. The graph indicates that the speed sensor B measurements between 90 and 120 degrees may be artifacts of tower shading. Similarly, sensor A may be shaded between about 190 and 220 degrees.

The radial lines between 180 and 210 degrees on the polar plot may indicate that the wind direction

sensor froze in position while the speed sensors continued taking measurements. Or, perhaps of one the

speed sensors froze during a period of consistent winds from a prevailing direction. By displaying a single

month of data at a time, you can find the month that contains the data for the icing event. For example, in the above graph, two possible icing events appear during 2005 between 180 and 210 degrees. The two

graphs below show that the events were in January 2005 and December 2005. You can explore these

possible icing events in more detail and make data revisions on the Quality Control window.



See also






This window displays a data coverage chart that shows the location of gaps in each column of the data set.

Note that you can fill gaps in the Fill Gaps window and the Quality Control window.

See also

Data coverage chart

Fill Gaps window



Contact: [email protected] Last modified: September 16, 2008

Data Coverage Window

This window helps to clarify how Windographer determines the wind power class. The wind power classes are defined by ranges of mean wind power density at 50m above ground. The prerequisite to determining

the wind power class is therefore the calculation of the mean wind power density at 50m.

For a data set that contains wind speed measurements at multiple heights above ground, Windographer

calculates the mean wind power density at each height. If two or more wind speed sensors have the same

height, Windographer will average them to find the mean wind power density at that height. The resulting mean wind power densities appear in the table in the top right corner of the window and as diamonds in

the graph.

Windographer uses linear least squares regression to find the straight line that best fits the graph of the

natural logarithm of mean wind power density versus the natural logarithm of height. The graph shows this line of best fit.

To find the best estimate of the mean wind power density at 50m, Windographer calculates the value of

mean power density at which the line of best fit crosses 50m above ground. Please see the article on

calculating the mean wind power density at 50m for details.

Note that Windographer calculates the mean wind power density at 50m from the

line of best fit even if the data set contains one or more wind speed sensors at 50m.

See also

Calculating the mean wind power density at 50m

Wind power density

Wind power class




Wind Power Class Analysis Window

The Extreme Wind Analysis window allows you to calculate the expected extreme wind speeds over periods of time up

to 100 years. It does this by finding the maximum wind speeds in each year of the data set, fitting a Gumbel

distribution to those values, and then calculating 50-year or 100-year return values from that best-fit Gumbel

distribution.

You can access this window by choosing Extreme Winds from the Analyze menu:

Wind Data

The drop-down box at the top left of the window lists the wind speed data columns in your data set. Windographer

searches the selected data column to find the maximum values that occur in each year of the data set. If the selected

wind speed data column has an associated maximum value column, Windographer will also search this maximum

value column, and in the table and graphs, it will identify this data as gust data.

Tip: You can associate a maximum value column with a wind speed data column in the

Configure Data Set window.

In the example below, the data column called 'Speed 40m' contains the 10-minute average wind speed at 40m and

another data column called '40m peak gust' contains the maximum 3-second gust within each time step.

The table on the right summarizes the annual data available for the chosen wind speed sensor for each year in the

data set. If the data set contains more than three years of data, the scroll bar on the right of the table will allow you

to scroll through all the years. The checkboxes allow you to exclude years that contain insufficient data.

To remove years with a low data recovery rate, check the checkbox labeled Whose data recovery rate is less than and

enter the acceptable rate. In the example above, setting a minimum data recover rate of 75% excludes both the 10-

minute data and the gust data from 2006. In the table, Windographer shades excluded data in pink.

To remove years with partial data, check the checkbox labeled Whose number of valid records is less than and enter the

minimum number of valid records. In the example above, the requirement of at least 2600 valid records excludes the

10-minute data from 2004.

Extreme Wind Analysis Window

Curve Fit

Use the radio buttons to choose whether to plot the 10-minute data or the gust data. This choice applies to the two

curve fit graphs only. Note that if the selected wind speed sensor has no associated maximum value column,

Windographer disables the radio buttons.

Two closely related graphs appear in the Curve Fit section of the window. The first graph plots the cumulative

distribution function of the measured data along with the best-fit Gumbel distribution. In this graph the y-axis

indicates the probability that the maximum annual 10-minute wind speed (or maximum annual gust) will be less than

or equal to a particular value.

The example below shows data from a 16-year data set of 10-minute mean wind speeds at 75m above ground. The

measured data points fall close to the best-fit Gumbel distribution, meaning the Gumbel distribution is a reasonable fit

to the data in this case. (It may not always fit the data this well.) The graph shows that according to the best-fit

Gumbel distribution, there is a 60% chance that in any given year, the maximum 10-minute wind speed will be less

than or equal to 26 m/s. Similarly, there is an 85% chance that it will be less than or equal to 28 m/s.

The second graph shows the same information in a slightly different way. In this graph the y-axis indicates the

probability that the annual peak wind speed will exceed a particular value. This probability of exceedence is equal to

one minus the value returned by the cumulative distribution function. The y-axis of this graph uses a logarithmic

scale. The example below shows that for any given year, the probability that the maximum wind speed will exceed 31

m/s is about 3%.

Below the probability of exceedence graph, Windographer displays the parameters of the best-fit Gumbel distribution,

along with the r² value, which indicates the goodness of fit. An r² value close to 1 indicates a good fit.

Results

The graph in the Results section shows the expected extreme wind speed versus the return period. The return period

is the reciprocal of the probability of exceedence, so a probability of exceedence of 2% corresponds to a return period

of 1 / 0.02 = 50 years.

The graph above shows that for a mean wind speed of 30 m/s, the probability of exceedence is 4.8%. Therefore the

return period for a wind speed of 30 m/s is 1 / 0.048 = 20.8 years. In other words, we would expect a 10-min mean

wind speed of 30 m/s or greater to happen once every 20.8 years. The graph below confirms that an extreme wind

speed of 30 m/s corresponds to a return period of about 21 years. The graph and table below show that that the 50-

year extreme 10-minute mean wind speed is 31.6 m/s , and the 50-year extreme gust is 36.7 m/s.

Right-click any graph to change its properties, copy the image to the clipboard, or export it to a file. For information

about exporting graphs from Windographer, please refer to the article on exporting graphs.

See also

Gumbel distribution


Data recovery rate

Cumulative distribution function




This window uses three different algorithms to fit the Weibull distribution to wind speed data, and plots them all on the same graph, along with the frequency histogram of the measured wind speed data. To

access this window, choose Frequency Distribution from the Analyze menu.

Use the drop-down box labeled Wind speed sensor to choose the wind speed data to analyze. Use the filter

capabilities to analyze the frequency distribution for a particular wind direction or specific year and/or

month. Windographer displays the results in tabular and graphical format.

The following table briefly describes the three Weibull fitting algorithms. Click on one of the links for detailed information on any of the algorithms.

Filter Settings

Windographer allows you to perform a frequency distribution analysis on a portion of the data set - filtered

by wind direction, or date.

If your data set contains multiple wind direction sensors, Windographer will list them in the drop-down

box labeled Wind direction sensor. To filter for a particular wind direction sector, select a range of

directions from the drop-down box labeled Wind direction sector. By default, Windographer will display the number of direction that you specify in the preferred wind rose settings in the Options window, but you

can change the number of direction sectors with the drop-down box labeled Sectors.


specific month. If you select all years and all months, Windographer will not filter by date. If you select all

years and one particular month, Windographer will plot data from that month for all years in the data set. For example, if you choose April in a four-year data set, the graph will include data from all four Aprils. If

you select one particular year and all months, Windographer will plot data from only that year. If you

select a particular year and a particular month, Windographer will plot data for that single month.

Windographer automatically chooses a reasonable bin size, but you can enter a different bin size with the input box below the filters.

Frequency Distribution Analysis Window

Algorithm Description

Maximum

likelihood

algorithm

This iterative algorithm is the one that Windographer uses to calculate the Weibull parameters everywhere else in the

software.

Least squares

algorithm

Sometimes called the 'graphical method', this algorithm works by transforming the axes of the cumulative distribution

function so that a Weibull distribution would appear as a straight line, then finding the straight line that best fits the actual data.

WAsP algorithmThis algorithm, used by the WAsP wind flow model, finds the Weibull distribution which matches that actual distribution in

terms of two parameters: the mean wind power density and the proportion of values that exceed the mean.

Results

In the graph, Windographer plots the three Weibull distribution curves overlaid on the frequency

histogram of the actual measured data. In the table, it displays the following statistical parameters for each fitted distribution and for the actual measured data:

Discussion

The maximum likelihood algorithm and the least squares algorithm both attempt to fit a Weibull

distribution directly to the measured wind speed distribution, and they tend to produce very similar

results. The WAsP algorithm, however, takes a different approach and therefore sometimes produces significantly different results. The WAsP algorithm does not consider the shape of the actual wind speed

distribution, but rather considers only its mean wind power density and its proportion of values above the

mean. The Weibull distribution that the WAsP algorithm produces always matches these parameters

perfectly, but sometimes it does not closely match the actual wind speed distribution.

The graph below shows an example where the WAsP algorithm has produced a Weibull distribution that fits the measured distribution very poorly:

But as the table below shows, that Weibull distribution perfectly matches the measured data in terms of

mean wind power density and proportion above the mean:


Weibull k The shape factor of the two-parameter Weibull distribution.

Weibull c The scale factor of the two-parameter Weibull distribution.

Mean The arithmetic mean (average) value of the data or the fitted distribution.

Proportion Above

MeanThe fraction of values in the data set or the fitted distribution that exceed the mean value.

Power Density The mean wind power density of the data set or the fitted distribution.

R SquaredThe goodness-of-fit parameter that indicates how closely the fitted Weibull distribution matches the frequency histogram of

the measured data. Values approaching one indicate a good fit.

As this example suggests, your choice of Weibull fitting algorithm will depend on your objective. If you wish to find the Weibull distribution that best matches the measured wind speed distribution, you should

choose either the maximum likelihood algorithm or the least squares algorithm. If you wish to find the

Weibull distribution that best matches the measured wind power density and proportion above the mean,

you should choose the WAsP algorithm.

See also


Wind power density

Maximum likelihood algorithm

Least squares algorithm

WAsP algorithm




The Probability of Exceedence window analyzes the distribution of annual mean values for a particular data column, and calculates the probability that the annual mean will exceed certain values. It can use 5

or 10 years of data to estimate the probability that over the long term, the mean value for any given year

will exceed a certain value. This estimate becomes more accurate with more years of data.

You can access this window by choosing Probability of Exceedence from the Analyze menu.

When you choose a data column from the drop-down box, Windographer finds the mean value of that

data column in each year of the data set. It puts those annual mean values in the table at the left side of the window, along with the number of time steps that went into that calculation.

With the Month drop-down box, you can choose to analyze the data for a specific month. If you select All, Windographer will consider all the data from the entire data set. If you select one particular month,

Windographer will use data from that month for all years in the data set.

With the edit box labeled Minimum number of time steps, you can choose not to use data years with fewer

data points (time steps). In the example above, by setting the minimum number of time steps to 3600, we have excluded the year 2001 because it contains only 3,516 time steps. Above the table,

Windographer reports that five years contain sufficient time steps, which confirms that one of the six

years is not included in the analysis.

The Frequency Histogram graphically displays the frequency with which the annual mean values fall within

various bins. The example below shows that the mean wind speed fell between 8.8 m/s and 8.9 m/s in almost 30% of the years in the data set.

Probability of Exceedence Window

The Probability of Exceedence graph shows both the measured values and the normal distribution that

best fits those values. The y-axis indicates the probability that the annual mean value will exceed a

particular value. The example below shows data from a 16-year data set . The measured data points fall

close to the best-fit normal distribution, meaning the normal distribution is a good fit to the data in this case. (It may not always fit the data this well.) This graph shows that for any particular year, the

probability that the mean annual wind speed will exceed 8.8 m/s is about 50%, and the probability that it

will exceed 9.0 m/s is about 12%.

The table to the left of the graph shows the key percentile values for both the actual distribution and the

best-fit normal distribution. The table below shows that, according to the best-fit normal distribution, the P95 value is 8.519 m/s, which means that we would expect the mean annual wind speed to exceed 8.519

m/s in 95 years out of 100.

Note that Windographer requires many years of data to calculate all the percentiles for the actual distribution. It requires only a few years to fit the normal distrbution though, and from the normal

distribution it can calculate any percentile. To calculate the percentile values for the actual distribution

column, Windographer interpolates linearly between the values of the actual distribution, as shown in the

following explanation.

To find the P90 value, draw a horizontal line at 90% POE. The x-value at which that line intersects the actual distribution is the P90 value shown in the column labeled Actual Distribution. The x-value at which

that line intersects the normal distribution is the P90 value shown in the column labeled Normal

Distribution. The graph below shows that the P90 value from the actual distribution is about 8.55 m/s, and P90 value from the normal distribution is about 8.58 m/s. Those numbers confirm the P90 values shown

in the table above.

Note that this window differs from the Extreme Wind Analysis window. This window analyzes the annual mean values, which tend to conform to a normal

distribution. The Extreme Wind Analyis window analyzes the annual maximum

values, which tend to conform to a Gumbel distribution.

See also

The Standard Atmosphere Tool window plots temperature and pressure data given by the equations of the

International Standard Atmoshere, as well as the resulting air density given by the ideal gas law. The graph shows values over a range of elevations, but you can also see the specific values at a particular elevation by

typing an elevation into the Elevation box.

The following equation closely approximates the pressure of the International Standard Atmosphere up to an

elevation of 5,000m:

The following elevation closely approximates the temperature of the International Standard Atmosphere up to

an elevation of 11,000m:

Source for the above equations: Manwell et. al (2002). Windographer uses the above equations to estimate air temperature and/or air pressure for any time step in which the data set does not contain measured temperature or pressure data. (Temperature or pressure data

may be missing either because the data set does not include temperature or pressure data.) It does so to calculate the air density in each time step, which it uses to calculate wind power density and wind turbine

power output.



See also



Air density

Air pressure

Air temperature


Written by: Tom Lambert Contact: [email protected] Last modified: April 25, 2008

Standard Atmosphere Window

where:

p is the air pressure [kPa]

z is the elevation [m]

where:

T is the air temperature [K]

T0 is the standard sea-level temperature [288.16 K]

B is the standard lapse rate [0.00650 K/m]


This tool window allows you to generate one year of synthetic hourly wind speed data with the statistical properties you prescribe. Using the drop-down box at the top of the window you can choose between

three levels of detail. If you choose Simple you will need to specify the mean wind speed for each month

of the year, and the annual values of the following four parameters:

� Weibull k

� Autocorrelation coefficient

� Diurnal pattern strength

� Hour of peak wind speed

If you set the level of detail to Intermediate you must specify monthly values of mean wind speed and each

of the four parameters listed above. If you set the level of detail to Advanced, then for each month you

must specify the Weibull k, the autocorrelation coefficient, and the mean 24-hour wind speed profile.

Once you have chosen a level of detail and specified all the parameters, click the button labeled Generate and Export to create the hourly data set. Windographer will ask you to specify a file name to which to save the data set. The exported file is a text file with one column and 8,760 lines, each line containing the

hourly mean wind speed. The first line of data corresponds to the first hour of the year, meanig the one

starting at midnight, January 1st. The exported file is suitable for import into the HOMER micropower optimization model.

For a description of the process by which Windographer synthesizes wind speed, please see the article on synthesizing wind speed data.

See also

Weibull k

Autocorrelation coefficient

Diurnal pattern strength


Synthesizing wind speed data



Synthesize Wind Speed Data Tool

The wind shear tool window allows you to enter the wind speeds at different heights above ground and see the logarithmic and power law profiles that best fit the data you enter. The window displays the best-

fit values of surface roughness and power law exponent, and lets you enter another height at which it

estimates the wind speed based on the best-fit profiles.

If you have a data file open when you open the Wind Shear Tool window, Windographer will enter the

measured wind speed profile from the current document into the table of known data points.

For information about exporting graphs from Windographer, please refer to the article on exporting graphs.

See also

Wind shear


Surface roughness

Power law exponent

Exporting graphs


Contact: [email protected] Last modified: September 14, 2005

Wind Shear Tool Window

This window lets you estimate the annual energy production of a wind turbine in a wind regime that you describe with a few statistical parameters rather than with time series data.

Use the radio button in the top left corner to choose whether to specify the wind resource data using

annual mean values, monthly mean values, or a frequency histogram. You will need to enter the height at

which the wind speed data apply, and the power law exponent to define the wind shear between the

anemometer height and the wind turbine hub height.

Once you have described the wind resource, choose a wind turbine model and a hub height. Enter the various loss terms (described in the article on the Wind Turbine Output window) and click the Calculate Output button. The tables in the bottom right corner will display the mean power output, the annual

energy output, and the capacity factor before and after losses.

To estimate the energy output of a wind turbine in a wind regime for which you

have measured time series data, open the data set using File > Open and open

the Wind Turbine Output window.

Right-click any graph or table to copy it to the clipboard or export it to a file.

See also

Weibull k

Wind Turbine Output window





Wind Turbine Output Tool

The Options window allows you to specify a few settings that affect Windographer. To access this window, choose Options from the Tools menu:

General Settings

The Preferred wind shear profile selection determines the initial (or default) setting for wind shear graphs

throughout Windographer. Windographer will report the surface roughness if you choose the Logarithmic law, or the power law exponent if you choose the Power law. This selection will impact graphs on the Reports tab.

The Preferred edition of IEC standard 61400-1 for turbulence selection determines the initial setting for

turbulence analysis throughout Windographer. If you choose the 2nd edition IEC standards, Windographer

will display the characteristic turbulence intensity on turbulence graphs. If you choose the 3nd edition

standards, Windographer will display the representative turbulence intensity on turbulence graphs.

This selection will affect graphs on the Reports tab, and will affect the initial setting in the Turbulence Analysis window. You can also change the preferred setting for turbulence analysis from the Turbulence

Analysis window. If you have a different IEC selection when you close the Turbulence Analysis window,

Windographer will ask you whether you wish to change your preference.

Default Wind Rose Settings

These settings determine the initial (or default) appearance of the wind roses that Windographer displays

on the Wind Rose tab, the Summary tab, and the Reports tab, and some of the Analysis windows. On

Wind Rose tab you can override these default settings, but if you typically prefer a particular drawing style or number of sectors, you can specify those settings here to avoid having to specify them each time you

plot a wind rose.

Location of SDR Software

Windographer can import .RWD files written by the Symphonie Data Logger by NRG Systems. You must

have the SDR software installed on you computer to do so. This edit box allows you to specify the location of that software.

Options Window

Append Settings

These settings allow you to specify how Windographer should handle the scenario in which you append

data that overlap the existing data in time. You can choose among four possible alternatives:

1. The appended data never overwrites the existing data.

2. The appended data only overwrites existing data in time steps in which the existing data is

missing. In this case, the appended data can fill gaps in the existing data, but it cannot overwrite

any values.

3. The appended data overwrites existing data in all time steps except those in which the appended

data is missing.

4. The appended data always overwrites the existing data.

See also

Reports tab

Surface roughness

Power law exponent




Wind Rose tab

Importing .RWD files

Appending data




The air density is defined as the mass of a quantity of air divided by its volume. To calculate the air density in each time step, Windographer uses the ideal gas law, one form of which is:

Solving for density gives:

For air, which has a molar mass of 28.9664 kg/kmol, we can write the equation as follows:

Windographer uses that equation to calculate the air density in each time step. If the data set includes a

temperature column, Windographer will use that temperature data in the above equation. If the data set does not include a temperature column, or in time steps where the temperature column does not contain

data, Windographer estimates the temperature based on the site elevation, as described in the article on

air temperature. Similarly, if the data set does not include an air pressure column, or in time steps

where the air pressure column does not contain data, Windographer estimates the pressure based on the site elevation, as described in the article on air pressure.

Windographer adds the air density column to the data set as a calculated column. Windographer uses

the air density to calculate the wind power density and the wind energy content. You can view the air

density in any of the graphic or tabular formats that Windographer provides for any other data column. The example below shows a DMap of the air density over one year. The yellows and reds indicating high

air density correspond to the coldest parts of the year, while the blues indicating low air density

correspond to the warmest times of the year. You can create DMaps on the DMap tab.

Air Density

where:

p is pressure [kPa]

ρ is density [kg/m3]

R is the universal gas constant [8.314472 m3·kPa·K-1·kmol-1]

M is the molar mass [kg/kmol]

T is temperature [K]

where:

p is the air pressure [kPa]

T is the air temperature [K]

See also

Air pressure

Air temperature

Wind power density

Wind energy content


Last modified: October 31, 2007

The air pressure (also called atmospheric pressure, barometric pressure, or just pressure) is the force per unit area exerted against a surface by the weight of the air molecules above that surface. Windographer

uses the air pressure to calculate the air density in each time step.

If one of the data columns in the data set contains measured air pressure data, Windographer uses that

measured air pressure data when calculating the air density in each time step.

Tip: Measured air pressure data should not be adjusted to sea level.

If the data set does not contain measured air pressure data, or the air pressure column does not contain data for the time step in question, Windographer calculates the air pressure from the elevation according

to the following equation, which closely approximates the pressure of the International Standard

Atmosphere up to an elevation of 5,000m:

where z is the elevation in metres. Source: Manwell et. al (2002).

The graph below plots this equation versus elevation:

Barometric pressure does not vary drastically with weather conditions, but depends mainly on the elevation. So the International Standard Atmosphere fairly accurately predicts the true barometric

pressure.

See also

Air temperature

Air density

Elevation

Air Pressure

Windographer uses the air temperature (also called ambient temperature) to calculate the air density in each time step.

If one of the columns in the data set contains the air temperature, Windographer uses the appropriate

value from that data column when performing calculations. (If the temperature units are not Kelvin,

Windographer first converts the temperature to K.)

If the data set does not contain air temperature data, or the air temperature data column does not

contain data for the time step in question, Windographer calculates the air temperature from the elevation according to the following equation, which closely approximates the temperature of the International

Standard Atmosphere up to an elevation of 11,000m:

Source: Manwell et. al (2002).

The graph below plots this equation versus elevation:

Note: Temperature varies strongly with seasonal weather patterns. The constant

temperature given by the International Standard Atmosphere only roughly approximates the true temperature. If your data set contains measured

temperature data, but values are missing for some time steps, you can use the

Quality Control window to fill the gaps with data that is statistically similar to the measured data. That technique should produce more accurate temperature data

than does the International Standard Atmosphere equation.

Air Temperature

where:


T0 is the standard sea-level temperature [288.16 K]

B is the standard lapse rate [0.00650 K/m]

See also

Air pressure

Air density

Elevation





Wind speed data typically exhibit some amount of autocorrelation, which can be defined as the degree of dependence on preceding values. For a time series z

1, z2, z3, ..., z

N, we can define an autocorrelation

coefficient rk as follows:

The autocorrelation coefficient rk represents the degree to which the value in one time step tends to

depend on the value k time steps in the past. For example, if r3 were equal to 0.8, that would indicate that

the value in one time step is 80% determined by the value three time steps in the past, and 20%

determined by randomness and other factors. By definition, r0 = 1.

For each anemometer in the data set, Windographer calculates the autocorrelation coefficient for one hour of lag, meaning it calculates the value of r

k for a value of k equal to the number of time steps in one hour.

For a data set with a time step of 10 minutes, k = 6. For 30-minute data, k = 2, and for 60-minute data k

= 1. The one-hour autocorrelation coefficient is undefined if the time step is greater than one hour.

Windographer displays the one-hour autocorrelation coefficient in the Wind Speed Sensor Summary table.

The graph below illustrates the effect of autocorrelation on wind speed data. Both data sequences shown

in the graph are hourly wind speed sequences synthesized using Windographer's Synthesize Data window. The two sequences have the same Weibull distribution, and are different only in the degree of

autocorrelation. The one-hour autocorrelation coefficient is a very low 0.50 for the sequence plotted in

orange, and a very high value of 0.99 for the sequence plotted in blue. Periods of high and low winds tend to persist much longer in the the strongly autocorrelated sequence.

One-Hour Autocorrelation Coefficient

where:

rk is the autocorrelation factor for a time lag of k time steps

N is the number of time steps

zi is the value in time step i

is the average value over the N time steps

Real measured wind speed sequences typically exhibit one-hour autocorrelation coefficients between 0.65

and 0.98.

See also

Wind Speed Sensor Summary table

Synthesize Data window





In Windographer, a data set consists of one or more original data columns plus one or more calculated columns. An original data column is a column of recorded data that comes from the data file. An original

data column might contain, for example, the air temperature at 2m above ground, or the wind speed at

30m above ground. A calculated column is one that Windographer creates from calculations on the

original data columns. A calculated column might contain, for example, the air density, the wind power density at 30m, or the turbulence intensity at 50m. You can view a calculated column's data in any of the

graphic and tabular formats available in Windographer.

Windographer adds the following calculated columns to the data set:

� The air density.

� The surface roughness (if the data set contains wind speed data from two or more heights above

ground).

� The power law exponent (if the data set contains wind speed data from two or more heights above ground).

� The wind power density for each wind speed data column.

� The turbulence intensity for each wind speed data column that has an associated standard

deviation column.



Calculated Columns

The calm threshold is a wind speed value below which Windographer considers the wind to be 'calm'. You specify the calm threshold on the Configure Data Set window.

The value of the calm threshold affects wind rose diagrams. When drawing wind frequency roses, which

show the frequency with which the wind blows from each direction sector, Windographer includes only

those direction observations that correspond to wind speeds above the calm threshold. (In data sets that

contain data from more than one anemometer, Windographer will refer to the anemometer closest in height to the plotted wind direction sensor when checking whether the wind speed exceeds the calm

threshold.) The calm frequency, or the percentage of time that the wind speed is at or below the calm

threshold, appears at the top right of all wind frequency rose diagrams, as highlighted in the example below:

The calm threshold also affects the 'frequency of calms' variable that appears in the Wind Speed Sensor Summary table.

See also





Calm Threshold

The capacity factor of a wind turbine is equal to its average power output divided by its rated power. The average power output is often calculated over one year or one month, but it could be calculated over any

period.

On the Wind Turbine Output window Windographer displays overall average capacity factor (averaged

over the entire data set) and the average capacity factor for each month of the year.

See also

Rated power





Capacity Factor

The cumulative distribution function F(x) gives the probability that a variable will take on a value less than

or equal to a number x.

The cumulative distribution function is also called the cumulative density function or simply the

distribution function. Within Windographer, we sometimes abbreviate cumulative distribution function as CDF.

See also




Cumulative Distribution Function

For a set of 10-minute time steps, each of which contains a value of turbulence intensity, the characteristic turbulence intensity is equal to the 84th percentile of the turbulence intensity values,

meaning the value below which 84% of the values fall. If the turbulence intensity values conform to a

normal distribution, the 84% percentile is equal to the mean plus one standard deviation.

Windographer does assume that the turbulence intensity values are normally distributed, so it calculates

the characteristic turbulence intensity as the mean plus one standard deviation.

For example, the following table contains 30 time steps of data on mean wind speed, standard deviation of wind speed, and turbulence intensity, which is the ratio of the two:

Characteristic Turbulence Intensity

Date Time

Mean

Wind

Speed

Std. Dev. of

Wind Speed

Turbulence

Intensity

12/22/2007 15:30:00 14.5 1.2 0.0828

12/22/2007 15:40:00 14.2 1.3 0.0915

12/22/2007 15:50:00 12.6 1.2 0.0952

12/22/2007 16:00:00 12.3 1.0 0.0813

12/22/2007 16:10:00 13.3 0.8 0.0602

12/22/2007 16:20:00 13.0 1.0 0.0769

12/22/2007 16:30:00 13.4 1.0 0.0746

12/22/2007 16:40:00 13.7 1.0 0.0730

12/22/2007 16:50:00 12.8 1.0 0.0781

12/22/2007 17:00:00 13.7 1.0 0.0730

12/22/2007 17:10:00 14.2 0.9 0.0634

12/22/2007 17:20:00 13.9 0.7 0.0504

12/22/2007 17:30:00 13.8 0.9 0.0652

12/22/2007 17:40:00 13.6 1.1 0.0809

12/22/2007 17:50:00 13.1 1.0 0.0763

12/22/2007 18:00:00 12.2 1.1 0.0902

12/22/2007 18:10:00 12.5 1.0 0.0800

12/22/2007 18:20:00 12.4 0.8 0.0645

12/22/2007 18:30:00 12.7 0.8 0.0630

12/22/2007 18:40:00 12.3 1.1 0.0894

12/22/2007 18:50:00 12.1 0.7 0.0579

12/22/2007 19:00:00 12.7 0.7 0.0551

12/22/2007 19:10:00 11.4 1.0 0.0877

12/22/2007 19:20:00 12.1 1.0 0.0826

12/22/2007 19:30:00 11.6 0.8 0.0690

12/22/2007 19:40:00 11.4 0.9 0.0789

12/22/2007 19:50:00 10.7 0.7 0.0654

12/22/2007 20:00:00 11.0 0.8 0.0727

12/22/2007 20:10:00 10.8 0.8 0.0741

12/22/2007 20:20:00 11.7 0.7 0.0598

Those 30 turbulence intensity values (highlighted in the table) have a mean of 0.0738 and a standard

deviation of 0.0113. Windographer would therefore calculate the characteristic turbulence intensity over that period as 0.0738 + 0.0113 = 0.0851.

The 2nd edition of IEC standard 61400-1 defined standard turbulence categories based on the value of the

characteristic turbulence intensity at 15 m/s. For more information, please see the article on the

Turbulence Analysis window.

See also







For each wind speed column in the data set, Windographer calculates the coefficient of variation (sometimes called the relative standard deviation) using the following equation:

Windographer reports the coefficient of variation for each wind speed data column in the Wind Speed

Sensor Summary table, which you can generate on the Tables tab of the main window.

See also

Standard deviation

Tables tab




Coefficient of Variation

where:

σ is the standard deviation of the wind speed distribution

is the long-term average wind energy speed [same units as σ]

A data coverage chart shows the time segments in which data columns do or do not contain data. The example below shows that:

� all twelve data columns contain data from mid-2001 to the end of 2004

� all twelve data columns contain a gap of about one month in mid-2003, and two shorter gaps in

2004

� most data columns contain four more short gaps, two in 2003 and two in 2004

� the 'Direction 45m' data columns contain a three-month gap in early 2002

Windographer displays a data coverage chart in the Fill Gaps window and in the Data Coverage window.

See also

Fills Gaps window

Data Coverage window


Contact: [email protected] Last modified: January 20, 2009

Data Coverage Chart

Windographer uses the following equation to calculate the data recovery rate over a particular time interval:

In this context, a 'valid record' is a time step which contains a data value, as opposed to a time step in

which the data value is missing for one reason or another. The possible number of records is equal to the

number of valid records plus the number of missing records.

Note that when calculating the data recovery rate for a month or a year, the

possible number of records refers to the number of time steps that the data set

covers within that month or year, which may be fewer than the total number of time steps within the month or year. For example, if the data set begins halfway

through April, then the possible number of records in April will be half of the total

number of time steps in April.

Windographer reports the data recovery rate in the annual statistics table, the monthly statistics table,

and the wind speed sensor summary table. The links below give additional information about these tables.

See also

Tables tab







Data Recovery Rate

where:

Nvalid is the number of valid records in the time interval

Npossible is the possible number of records in the time interval (the number of time steps in the time interval)

The diurnal pattern strength is a measure of how strongly the wind speed depends on the time of day. To calculate this factor Windographer fits a cosine wave to the daily wind speed profile, as the graph below

illustrates:

The diurnal pattern strength is equal to the amplitude of this cosine wave divided by its average value. The hour of peak wind speed is the hour in which this best-fit cosine wave peaks.

In the above example, the amplitude of the cosine wave is 2.70 km/hr and its average is 19.02 km/hr, so

the diurnal pattern strength is 0.142. The cosine wave peaks in the 15th hour of the day, so the hour of

peak wind speed is 15.

Windographer calculates the diurnal pattern strength and the hour of peak wind speed for each wind speed data column in the data set, and displays that information in the Wind Speed Sensor Summary

table.

See also





Diurnal Pattern Strength

The elevation (also called altitude) is the height above sea level. You can specify the elevation in metres or feet on the Configure Data Set window. If the data set does not contain data on air pressure,

Windographer uses the elevation to estimate it based on the international standard atmosphere. Similarly,

if the data set does not contain data on air temperature, Windographer estimates it from the elevation

using the international standard atmosphere.

In a data set that does contain an air temperature column, Windographer will use the elevation to estimate the temperature only in time steps for which the air temperature column contains no data.

Likewise, in a data set that contains an air pressure column, Windographer will use the elevation to

estimate the pressure only in time steps for which the air pressure column contains no data.

Windographer uses the temperature and pressure to calculate the air density.

See also

Air pressure

Air temperature

Air density





Elevation

For each wind speed data column, Windographer calculates the energy pattern factor (also called the cube factor) using the following equation:

If one were to assume constant air density, the energy pattern factor would be the ratio of the actual

mean wind power density to the wind power density one would calculate using only the mean wind speed:

Windographer displays the energy pattern factor for each wind speed sensor in the Wind Speed Sensor

summary table.

See also





Energy Pattern Factor

where:


Ui is the wind speed at time step i

is the mean wind speed [same units as vi]

where:

is the mean wind power density [W/m3]

ρ is the air density [kg/m3]

is the mean wind speed [m/s]

Ke is the energy pattern factor

A gap is a set of one or more time steps that contain no data. A time step might contain no data because:

� the raw data file did not specify a value for that time step

� the value was removed by a filter specified on the Configure Data Set window, or

� the value was removed by a deletion in the Quality Control window

Windographer can accept any number of gaps in a data set. Gaps do not prevent

Windographer from displaying data in graphical or tabular format, nor do they prevent it from performing any calculations.

In time series graphs, gaps appear as missing line segments. The example below shows a four-hour gap late in the day on July 14:

In a data coverage chart, gaps appear as blank segments. The example below shows a large gap in the

'Direction 45m' data column in early 2002, a large gap in all data columns in mid-2003, and several smaller gaps:

You may choose to fill gaps with synthetic data. In Windographer, you can do that in the Fill Gaps window

or in the Quality Control window.

See also

Fill Gaps window



Data coverage chart

Gap

The Gumbel distribution is useful for modeling the probability of extreme wind speeds. The following equations give the probability distribution function and the cumulative probability distribution function of

the Gumbel distribution:

where x is the extreme value, β is a scale parameter, and µ is a mode parameter. Both parameters have

the same units as x.

As its name implies, the mode parameter specifies the most probable value of x. The graph below shows

the probability distribution function for three Gumbel distributions, all with a scale parameter of 8, but

with the mode parameter varying from 15 to 45:

The scale parameter determines the breadth of the distribution. The graph below shows the probability distribution function for five Gumbel distributions, all with a mode parameter of 30 but with the scale

parameter varying from 4 to 12:

Gumbel Distribution

Return Period

The return period is a useful concept in the field of extreme value analysis. The return period is defined as

the reciprocal of the probability of exceedence. For example, say that a given Gumbel distribution

represents the distribution of annual extreme wind speeds, and that when x takes a value of 35 m/s, the

cumulative distribution function F(x) gives a value of 0.9875. That means that there is a 98.75%

probability that in any one year, the annual extreme wind speed x will be equal to or less than 35 m/s.

The probability of exceedence of 35 m/s is therefore 100% - 98.75% = 1.25%. The resulting return

period is 1 / 0.0125 = 80 years. That means we would expect an extreme wind speed of 35 m/s once in

80 years.

Analysts typically want to know the annual extreme value corresponding to a particular return period,

such as 50 years or 100 years. The following equation gives the annual extreme value for a specified Gumbel distribution and a specified return period:

where R is the return period in years.

In the Extreme Wind Analysis window, Windographer fits a Gumbel distribution to the annual extreme wind speed data, and calculates the extreme wind speeds expected for a range of return periods.

See also




The gust factor is the ratio of the peak gust within a time step divided by the mean wind speed for that time step.

For each wind speed sensor for which you have indentified an associated gust column, Windographer

creates a calculated column containing the gust factor in every time step. It names this calculated column

the same as the wind speed sensor name, plus the letters "GF". For example, if the wind speed column

name is "Speed 60m", its associated gust factor column will have the name "Speed 60m GF". You can view the gust factor column in all the graphic and tabular formats that Windographer provides, as you can

for any other data column.

Tip: The Configure Data Set window allows you to indicate whether a wind speed

column has an associated gust column.

See also

Calculated column




Gust Factor

Windographer fits a cosine wave to the daily wind speed profile, as the graph below illustrates:

The hour of peak wind speed is the hour in which this best-fit cosine wave peaks. The diurnal pattern

strength is equal to the amplitude of this cosine wave divided by its average value.

In the above example, the amplitude of the cosine wave is 2.70 km/hr and its average is 19.02 km/hr, so

the diurnal pattern strength is 0.142. The cosine wave peaks in the 15th hour of the day, so the hour of peak wind speed is 15.

Windographer calculates the diurnal pattern strength and the hour of peak wind speed for each wind

speed data column in the data set, and displays that information in the Wind Speed Sensor Summary

table.

See also

Diurnal pattern strength





Hour Of Peak Wind Speed

The mean of monthly means (MMM) is an average of the twelve monthly averages. For seasonally-biased data sets, in which some months are over-represented and some are under-represented, the mean of

monthy means provides a more accurate estimate of the long-term mean than does a simple arithmetic

mean.

To calculate the mean of monthly means, Windographer first calculates the mean of all the January data

in the data set, then the mean of all the February data, then the mean of all the March data, and so on. It then multiplies each monthly mean by the appropriate weighting factor, shown below, then divides by

365.24. The monthly weighting factors account for the unequal sizes of the months. (Note that February

contains 28.24 days on average.)

The mean of monthly means is useful because it removes any bias in the data set resulting from unequal

representation of months. Imagine, for example, a 13-month set of temperature data that includes two

Augusts but only one of every other month. A simple arithmetric mean over the entire data set would therefore include twice as much data from August as it would from the other eleven months of the year. If

the mean August temperature is higher than the mean annual temperature, that simple arithmetic mean

would be erroneously high due to the over-representation of August. By contrast, the mean of monthly means gives the August mean the same weight as it does the July mean or the October mean. As a result,

the mean of monthly means should reflect the true long-term mean more accurately than does the simple

mean.

Windographer displays the mean of monthly means in the Monthly Statistics table.

See also




Mean of Monthly Means

MonthWeighting

Factor

January 31

February 28.24

March 31

April 30

May 31

June 30

July 31

August 31

September 30

October 31

November 30

December 31

365.24

The term negative wind shear refers to an atmospheric condition in which the wind speed decreases with height above ground. This is a reversal of the typical situation, in which the wind speed increases with

height. Negative wind shear can occur during calm conditions in hilly terrain as a result of cold air

drainage, a phenomenon whereby cold air flows down a slope because of its higher density relative to the

surrounding air. But in the flatter, windier locations that typically interest wind energy developers, negative wind shear is a very rare phenomenon, and apparent negative wind shear often turns out to be

an artifact of anemometer icing. For example, if the highest anemometer is the one most severely

affected by icing, it may as a result record a wind speed that is artificially low, perhaps lower than the other anemometers record. This apparent negative wind shear can serve as evidence for icing.

You can search for negative wind shear and remove iced data segments using the Quality Control window.

See also

Wind shear

Quality Control example




Negative Wind Shear

On the Wind Turbine Output window and the Wind Turbine Output Tool window you can enter four loss factors that specify the amount of energy lost due to several factors:

Windographer combines these four loss factors into the overall loss factor using the following equation:

Windographer uses the overall loss factor to calculate the net power and energy output of a wind turbine. For details please see the article on calculating the energy output of a wind turbine.

See also





Overall Loss Factor

where:

fdowntime is the downtime losses factor

farray is the array losses factor

ficing is the icing/soiling losses factor

fother is the other losses factor

The probability distribution function f(x) gives the probability that a variable will take on the value x. It is

often expressed using a frequency histogram, which gives the frequency with which the variable falls within certain ranges or bins.

The probability distribution function is also called the probability density function or simply the density

function. Within Windographer, we sometimes abbreviate probability distribution function as PDF.

See also





Probability Distribution Function

The power law exponent (sometimes called the power law coefficient) is a number that characterizes the wind shear, which is the change in wind speed with height above ground. The power law uses the power

law exponent as a parameter. The graph below shows the effect of the power law exponent on the wind

shear profile predicted by the power law. Each line on the graph corresponds to a different power law

exponent, indicated in the graph with the symbol α. In all cases, the wind speed is 10 m/s at 100m above

ground. The wind speed at lower heights decreases with increasing power law exponent.

For data sets that contain wind speed data for two or more different heights above ground, Windographer calculates the power law exponent from the observed wind shear profile. To do so, Windographer solves

for the value of the power law exponent that causes the power law profile profile to most closely fit the

measured wind shear profile.

Note: Windographer can calculate the wind shear only if the data set contains two

or more wind speed sensors at different heights. For data sets that contain wind speed data from only one height above ground, Windographer simply sets the

power law exponent equal to the default value of 0.14.

The shape of the wind shear profile typically depends on several factors, most notably the roughness of the surrounding terrain and the stability of the atmosphere. Since the atmospheric stability changes with

season, time of day, and and meteorological conditions, the power law exponent also tends to change in

time.

The value of the power law exponent that Windographer displays on the Summary tab is based on the overall wind shear profile, meaning the wind shear profile that Windographer calculates from the entire

data set. But Windographer also calculates the wind shear profile and corresponding power law exponent

for each month of the year, each hour of the day, each wind direction sector, and each individual time step. The Wind Shear Analysis window displays the results of these calculations.

Power Law Exponent

As with any calculated column, you can view the power law exponent data column in each time step using

all of the graphic and tabular formats that Windographer provides. You could, for example, plot the power law exponent versus the wind speed on the Scatterplot tab.

Windographer displays the overall best fit power law exponent on the Summary tab and the Data Set

Summary table on the Tables tab.

See also

Wind shear

Surface roughness

Summary tab

Tables tab


Calculated column



A probability transformation is a statistical procedure by which one modifies a set of numbers to conform to a desired probability distribution function.

To perform a probability transformation, Windographer first calculates the cumulative distribution function

of the original set of data -- we will refer to this as the 'original CDF'. Then for each original data point, it

performs the following steps:

1. It refers to the original CDF to calculate the percentile value corresponding to that original data

point

2. It refers to the desired CDF to calculate the transformed value corresponding to that same

percentile value

Let's look at an example to illustrate this process. Imagine that we have a set of data that conform to a normal distribution, and we want to transform it so that it conforms to a Weibull distribution.

(Windographer does exactly this when synthesizing wind speed data.)

If our normally-distributed data had a mean of zero and a standard deviation of 1, its probability

distribution function would look like so:

And its cumulative distribution function -- the original CDF -- would look like so:

Probability Transformation

Imagine that we wish to transform this data to fit a Weibull distribution with a mean value of 6 and a

Weibull k value of 2. Our desired probability distribution function would therefore look like so:

And our desired cumulative distribution function -- the desired CDF -- would look like so:

To transform each value in the original data set, we would refer to the original CDF to find its

corresponding y-value, then we would take that same y-value to the desired CDF and find its corresponding x-value.

An original value of zero, for example, corresponds to a CDF value of 0.5 on the original CDF. Looking at

the desired CDF, we find that the value corresponding to a CDF value of 0.5 is approximately 5. That

means that any zero value in the original data set gets transformed into a value of 5 in the transformed

data set. Similarly, an original value of -1 would be transformed to value of approximately 2.5, and an original value of 1.5 would be transformed to a value of approximately 10.

This example looks at transforming data from a normal distribution to a Weibull distribution, but with this

same probability transformation approach, we could transform from any distribution to any other

distribution.

See also



Synthesize Wind Speed Data Tool




The rated power of a wind turbine is its nameplate capacity, meaning the amount of power it produces under the wind speed and air density conditions at which the manufacturer rates the turbine. Under

different conditions, the wind turbine may produce a different amount of power.




Rated Power

For a set of 10-minute time steps, each of which contains a value of turbulence intensity, the representative turbulence intensity is equal to the 90th percentile of the turbulence intensity values,

meaning the value below which 90% of the values fall. If the turbulence intensity values conform to a

normal distribution, the 90% percentile is equal to the mean plus 1.28 standard deviations.

Windographer does assume that the turbulence intensity values are normally distributed, so it calculates

the representative turbulence intensity as the mean plus 1.28 standard deviations.

For example, the following table contains 30 time steps of data on mean wind speed, standard deviation of wind speed, and turbulence intensity, which is the ratio of the two:

Representative Turbulence Intensity

Date Time

Mean

Wind

Speed

Std. Dev. of

Wind Speed

Turbulence

Intensity

12/22/2007 15:30:00 14.5 1.2 0.0828

12/22/2007 15:40:00 14.2 1.3 0.0915

12/22/2007 15:50:00 12.6 1.2 0.0952

12/22/2007 16:00:00 12.3 1.0 0.0813

12/22/2007 16:10:00 13.3 0.8 0.0602

12/22/2007 16:20:00 13.0 1.0 0.0769

12/22/2007 16:30:00 13.4 1.0 0.0746

12/22/2007 16:40:00 13.7 1.0 0.0730

12/22/2007 16:50:00 12.8 1.0 0.0781

12/22/2007 17:00:00 13.7 1.0 0.0730

12/22/2007 17:10:00 14.2 0.9 0.0634

12/22/2007 17:20:00 13.9 0.7 0.0504

12/22/2007 17:30:00 13.8 0.9 0.0652

12/22/2007 17:40:00 13.6 1.1 0.0809

12/22/2007 17:50:00 13.1 1.0 0.0763

12/22/2007 18:00:00 12.2 1.1 0.0902

12/22/2007 18:10:00 12.5 1.0 0.0800

12/22/2007 18:20:00 12.4 0.8 0.0645

12/22/2007 18:30:00 12.7 0.8 0.0630

12/22/2007 18:40:00 12.3 1.1 0.0894

12/22/2007 18:50:00 12.1 0.7 0.0579

12/22/2007 19:00:00 12.7 0.7 0.0551

12/22/2007 19:10:00 11.4 1.0 0.0877

12/22/2007 19:20:00 12.1 1.0 0.0826

12/22/2007 19:30:00 11.6 0.8 0.0690

12/22/2007 19:40:00 11.4 0.9 0.0789

12/22/2007 19:50:00 10.7 0.7 0.0654

12/22/2007 20:00:00 11.0 0.8 0.0727

12/22/2007 20:10:00 10.8 0.8 0.0741

12/22/2007 20:20:00 11.7 0.7 0.0598

Those 30 turbulence intensity values (highlighted in the table) have a mean of 0.0738 and a standard

deviation of 0.0113. Windographer would therefore calculate the representative turbulence intensity over that period as 0.0738 + 1.28 * 0.0113 = 0.0883.

The 3nd edition of IEC standard 61400-1 defined standard turbulence categories based on the value of the

representative turbulence intensity at 15 m/s. For more information, please see the article on the

Turbulence Analysis window.

See also







The roughness class is a dimensionless number based on the value of the surface roughness. The following equation defines the roughness class C:

where z0 is the surface roughness in m.

The following graph illustrates this relationship:

The following table lists the terrain types characteristic of various values of roughness class. Source:

Wind Energy Reference Manual at www.windpower.org.

Roughness Class

Roughness Class

Surface

Roughness (m)

Landscape Type

0 0.0002 Water surface

0.5 0.0024Completely open terrain with a smooth surface, e.g. concrete runways in airports, mowed grass, etc.

1 0.03Open agricultural area without fences and hedgerows and very scattered buildings. Only softly rounded hills.

1.5 0.055 Agricultural land with some houses and 8 metre tall

sheltering hedgerows with a distance of approx. 1250

Windographer displays the roughness class on the Summary tab and the Data Set Summary table.

See also

Surface roughness

Summary tab

Data Set Summary table




metres.

2 0.1

Agricultural land with some houses and 8 metre tall

sheltering hedgerows with a distance of approx. 500 metres.

2.5 0.2

Agricultural land with many houses, shrubs and plants, or 8

metre tall sheltering hedgerows with a distance of approx. 250 metres.

3 0.4Villages, small towns, agricultural land with many or tall sheltering hedgerows, forests and very rough and uneven

terrain.

3.5 0.8 Larger cities with tall buildings.

4 1.6 Very large cities with tall buildings and skycrapers.

For any data column in the data set, Windographer calculates the standard deviation using the following equation:

Windographer reports standard deviations in the Annual Statistics, Monthly Statistics, Directional Statistics, and Wind Speed Sensor Summary tables. You can generate these tables on the Tables tab of

the main window.

Tip: The overall standard deviation for a wind speed data column represents the

breadth of the distribution of wind speeds over a long period, such as a month, a

year, or the entire data set. It is different from the data column that contains the standard deviation of wind speeds within each time step, and the turbulence

intensity which Windographer calculates from that data column.

See also

Coefficient of variation


Tables tab








Standard Deviation

where:

zi is the average value in time step i

is the long-term average value


The surface roughness (sometimes called surface roughness length or just roughness length) is a number that characterizes the wind shear, which is the change in wind speed with height above ground. The

logarithmic law uses the surface roughness as a parameter. The graph below shows the effect of the

surface roughness on the wind shear profile predicted by the logarithmic law. Each line on the graph

corresponds to a different value of surface roughness, indicated in the graph with the symbol z0. In all

cases, the wind speed is 10 m/s at 100m above ground. The wind speed at lower heights decreases with

increasing surface roughness.

Different types of terrain are characterized by different values of surface roughness. The following table

shows typical surface roughness values for several types of terrain. Source: Manwell et al. (2002).

Reproduced with permission.

For data sets that contain wind speed data for two or more different heights above ground, Windographer

calculates the surface roughness from the observed wind shear profile. To do so, Windographer solves for

Surface Roughness

Terrain Description Surface Roughness (m)

Very smooth, ice or mud 0.00001

Calm open sea 0.0002

Blown sea 0.0005

Snow surface 0.003

Lawn grass 0.008

Rough pasture 0.01

Fallow field 0.03

Crops 0.05

Few trees 0.10

Many trees, few buildings 0.25

Forest and woodlands 0.50

Suburbs 1.50

Centers of cities with tall buildings 3.00

the value of surface roughness that causes the logarithmic wind shear profile to most closely fit the

measured wind shear profile. For data sets that contain wind speed data from only one height above ground, Windographer cannot calculate the surface roughness so it simply sets it equal to the default

value of 0.01m.

The wind shear profile is affected not only by the roughness of the surrounding terrain, but also by other

factors, most notably the stability of the atmosphere. Since the stability of the atmosphere changes with

season, time of day, and meteorological conditions, the shape of the wind shear profile (and hence the surface roughness) also changes in time.

The value of surface roughness that Windographer displays on the Summary tab is based on the overall

wind shear profile, meaning the wind shear profile that Windographer calculates from the entire data set. But Windographer also calculates the wind shear profile and corresponding surface roughness for each

month of the year, each hour of the day, each wind direction sector, and each individual time step. The

Wind Shear Analysis window displays the results of these calculations. These calculations are only possible for data sets that contain two or more anemometers at different heights.

As with any calculated column, you can view the data column containing the surface roughness in each time step using all of the graphic and tabular formats that Windographer provides. The example below

shows a scatterplot of the surface roughness versus the solar radiation. The graphs illustrates a common

phenomenon, which is that the highest surface roughness values typically coincide with the lowest values of solar radiation. That is because high levels of solar radiation tend to cause greater atmospheric

instability and mixing of layers, which causes the wind speeds in the different vertical layers to be

relatively consistent, resulting in low surface roughness values. During times of low or no solar radiation, the atmosphere tends to be much more stable so the layers do not mix, and the wind speed tends to

increase sharply with increasing height above ground, resulting in high surface roughness values. You can

create scatterplots on the Scatterplot tab.

Windographer displays the overall best-fit surface roughness on the Summary tab and in the Data Set

Summary table.

Note that Windographer always calculates the surface roughness in metres, regardless of the units used

for height above ground.

See also

Wind shear

Power law exponent

Roughness class

Summary tab

Scatterplot tab



Calculated column



The turbulence intensity, or TI, is a dimensionless number defined as the standard deviation of the wind speed within a time step divided by the mean wind speed over that time step. Windographer calculates

the turbulence intensity in each time step using the following equation:

For each wind speed sensor for which you have identified an associated standard deviation column,

Windographer creates a calculated column containing the turbulence intensity in every time step. It

names this calculated column the same as the wind speed sensor name, plus the letters "TI". For example, if the wind speed column name is "Speed 60m", its associated turbulence intensity column will

have the name "Speed 60m TI".

Tip: The Configure Data Set window allows you to indicate whether a wind speed

column has an associated standard deviation column.

You can view turbulence intensity column in all the graphic and tabular formats that Windographer provides for any other data column. The example below shows a scatterplot of the turbulence intensity

versus the wind speed. The graph shows a typical phenomenon: the highest values of turbulence intensity

tend to correspond to the lowest wind speeds. You can create scatterplots on the Scatterplot tab.

For a detailed look at turbulence, see the Turbulence Analysis window.

See also

Turbulence Intensity

where:

Ui is the average wind speed in time step i

σι is the standard deviation of the wind speed within time step i [same units as Ui]




Calculated column

Scatterplot tab



The uppermost wind speed sensor is the wind speed sensor with the highest measurement height. For instance, if your data set contains wind speed sensors at 10m, 30m and 60m above ground, the 60m

sensor is the uppermost.

By default, many of the graphs that Windographer produces show data from the uppermost wind speed

sensor.

Note that you can see and modify the measurement height of any wind speed or direction sensor in the

Configure Data Set window.

See also


Reports tab



Uppermost Wind Speed Sensor

To find the mean wind direction over some time interval, Windographer calculates a vector mean, also called a vector average. One calculates a vector mean by adding multiple vectors together tip to tail, and

calculating the direction of the resultant vector.

The example below shows the vector mean of three vectors: the first with a length of 38 and a direction of

320°, the second with a length of 90 and a direction of 45°, and the third with a length of 20 and a

direction of 340°. The resultant vector, which appears black in the diagram, has a vector mean direction of 16.2°.

Note: A simple calculation of the scalar mean direction often gives erroneous results. In the example above, the scalar mean direction is 235° (one third the

sum of 320°, 45°, and 340°). This is virtually the opposite direction of the true

vector mean.

To calculate a vector mean direction over several time steps for a particular wind direction sensor,

Windographer constructs a wind speed vector for each time step and adds these vectors tip to tail as

shown above. To calculate the wind speed vector for a particular time step, Windographer uses the value of wind speed from the wind speed sensor that is nearest in height to the wind director sensor of interest.

Windographer reports vector mean wind directions in the graph on the Time Series tab (when showing

daily, monthly, or annual averages) and in the Annual Statistics and Monthly Statistics reports on the

Tables tab.

See also

Tables tab

Time Series tab


Vector Mean

Wind analysts typically use the Weibull distribution to characterize the breadth of the distribution of wind speeds. The following equations give the probability distribution function and the cumulative

distribution function of the two-parameter Weibull distribution:

where U is the wind speed, k is a unitless shape factor, and c is a scale factor with the same units as U.

The following equation gives the relationship between the scale factor c and the long-term average wind

speed:

where is the long-term average wind speed and Γ is the gamma function.

The Weibull k value reflects the breadth of the distribution; the broader the distribution, the lower the

value of the Weibull k. The graph below shows several Weibull distributions, all with an average wind

speed of 7 m/s, but with the Weibull k value varying from 1.5 to 3.5.

Weibull Distribution

The Weibull distribution often fits measured wind speed distributions well. The graph below shows a

measured wind speed distribution along with the best-fit Weibull distribution:

Windographer uses the maximum likelihood algorithm to fit a Weibull distribution to a measured wind

speed distribution.

See also

Weibull k

Weibull c

Maximum likelihood algorithm



The Weibull c value is the scale factor from the Weibull distribution. This factor is related to the average wind speed by the following equation:

Windographer calculates the best-fit Weibull parameters using the maximum likelihood method (Stevens and Smulders, 1979). For details, please see the article on the Weibull distribution.

The Weibull parameters appear on the PDF tab and in numerous tables on the Tables tab.

See also

Weibull k


Tables tab



Weibull c

where:

is the average wind speed

Γ is the gamma function

k is the Weibull k factor

The Weibull k value is the unitless shape factor from the Weibull distribution. This factor reflects the breadth of the distribution, with lower values corresponding to broader distributions. The graph below

shows five Weibull distributions, all with an average wind speed of 7 m/s. Lower k values correspond to

broad distributions where the wind speed tends to vary widely, whereas higher k values correspond to

tighter distributions where the wind speed tends to stay within a narrower range.

To calculate the best-fit Weibull parameters, Windographer uses the maximum likelihood method (Stevens and Smulders, 1979). For details, please see the article on the Weibull distribution.

Windographer displays the best-fit Weibull parameters on the PDF tab and in the Annual Statistics,

Monthly Statistics, Directional Statistics, and Wind Speed Sensor Summary tables. You can create these

tables on the Tables tab.

See also


Weibull c

PDF tab

Tables tab







Weibull k

For each wind speed column in the data set, Windographer calculates the average wind energy content using the following equation:

Windographer displays the mean wind energy content for each wind speed sensor in the Wind Speed

Sensor Summary table.

See also

Wind power density





Wind Energy Content

where:

is the average wind energy content [kWh/m2/yr]

is the average wind power density [W/m2]

The wind power class is a number indicating the mean energy content of the wind resource. Wind power classes are based on the mean wind power density at 50 metres above ground, according to the following

table:

Source: Wind Energy Resource Atlas of the United States

(http://rredc.nrel.gov/wind/pubs/atlas/tables/A-8T.html)

Windographer classifies any wind resource with an average wind power density above 2000 W/m2 as class 8.

Windographer displays the wind power class on the Summary tab and in the Data Set Summary table.

See also

Calculating the mean wind power density at 50m

Wind power density

Summary tab




Wind Power Class

Wind Power Class Description Power Density at 50m (W/m2)

1 Poor 0-200

2 Marginal 200-300

3 Fair 300-400

4 Good 400-500

5 Excellent 500-600

6 Outstanding 600-800

7 Superb 800-2000

For each wind speed column in the data set, Windographer calculates the wind power density at each time step using the following equation:

For each wind speed sensor, Windographer creates a calculated column containing the wind power density in every time step. It names this calculated column the same as the wind speed sensor name, plus the

letters "WPD". For example, if the wind speed column name is "Speed 60m", its associated wind power

density column will have the name "Speed 60m WPD". You can view the wind power density column in all the graphic and tabular formats that Windographer provides, as you can for any other data column.

Windographer calculates the overall mean wind power density for each wind speed sensor, and displays

this value in the Wind Speed Sensor Summary table. It also calculates the wind power density at 50m

above ground to determine the wind power class. For more information please refer to the article on the

wind power class.

See also

Air density

Wind power class

Wind energy content


Calculated column




Wind Power Density

where:

is the wind power density, or the power per unit area, within the time step [W/m2]

ρ is the air density within the time step [kg/m3]

U is the average wind speed within the time step [m/s]

Wind shear refers to the change in wind speed with height above ground. The wind speed tends to increase with the height above ground, as in the following example where the wind speed was measured

at eight different heights above ground, from 0.5 m to 49 m:

The variation in the wind speed with height above ground can be called the wind shear profile. In the field of wind resource assessment, analysts typically use one of two mathematical relations to characterize the

measured wind shear profile: the logarithmic profile (log law) and the power law profile (power law). Brief

descriptions of each follow.

Logarithmic Profile

The logarithmic law (or log law) assumes that the wind speed varies logarithmically with the height above

ground according to the following equation:

If you know the average wind speed for two or more heights above ground, you can do a curve fit on the

Wind Shear

where:

U(z) is the wind speed [m/s] at some height above ground z [m]

U* is the friction velocity [m/s]

k is von Karman's constant (0.4)

z0 is the surface roughness [m]

ln is the natural logarithm

above equation to find the surface roughness. Knowing the surface roughness and the wind speed at

some height z1, one can use the logarithmic law to calculate the wind speed at another height z

2 using the

following equation:

For data sets comprising two or more anemometers at different heights above ground, Windographer

solves for the surface roughness value that causes the logarithmic profile to best fit the measured wind shear profile. (To do so, Windographer uses a linear least squares algorithm to fit a straight line to the

graph of average wind speed versus the logarithm of the height above ground.) The following graph

compares the resulting best-fit logarithmic profile to the measured wind shear profile we saw earlier:

Power Law Profile

The power law assumes that the wind speed varies with the height above ground according to the following equation:

If you know the average wind speed for two or more heights above ground, you can do a curve fit on the above equation to find the power law exponent. Knowing the power law exponent and the wind speed at

some height z1, one can use the power law to calculate the wind speed at another height z

2 using the

following equation:

where:

U(z) is the average wind speed (in m/s) at some height above ground z (in m)

β is a constant [m/s]

α is the power law exponent

For data sets comprising two or more anemometers at different heights above ground, Windographer solves for the power law exponent that causes the power law profile to best fit the measured wind shear

profile. (To do so, Windographer uses a linear least squares algorithm to fit a straight line to the graph of

the logarithm of the average wind speed versus the logarithm of the height above ground.) The following

graph compares the resulting best-fit power law profile to the measured wind shear profile we saw earlier:

Note that although the power law profile fits the data more precisely than does the logarithmic law profile

in this case, in other cases the logarithmic law profile fits the data more precisely.

See also


Surface roughness

Power law exponent

Negative wind shear




A Windographer document contains:

� the data you have imported from one or more raw data files

� the data column types, names, colors, and other information you have entered in the Configure

Data Set window

� any synthetic data you have generated to fill gaps using the Quality Control window or the Gap

Filling window

� any data columns you have added to the data set using the Virtual Anemometer window or the


� a record of all the raw data files have imported and all the changes you have made to the data set

The Windographer document reflects all the changes you have made to the data set. So if you delete a section of data using the Quality Control window or the Delete Data window, that section will appear as a

gap when you view the Windographer document. Similarly, if you delete a data column using the Delete

Data window, that data column will no longer appear in the Windographer document. If you apply a slope or offset to a data column using the Modify Data Columns window, the Windographer document will

contain the modified, not the original, data.

Tip: You can see a record of all the changes you have made to the data set in the

Document History window. The History tab of the Quality Control window shows a

more detailed record of any changes you have made in the Quality Control

window.

When you save a Windographer document, the program creates a file with a .windog extension. These

files use a binary file format that is compact and fast to read and write, but only Windographer can read them.

You can export data from a Windographer document to a text file using the Export Data window. You can

also export data from individual tables and graphs to text files, image files, or the clipboard. See the

articles on exporting graphs and exporting tables for details.

Note that the Windographer help system uses the terms 'Windographer document' and 'data set' interchangeably.

See also


Appending data





Virtual Anemometer window

Exporting data

Exporting graphs

Exporting tables

Windographer Document

The process by which Windographer estimates the energy output of a wind turbine in the measured wind regime consists of four major steps. First, it estimates the wind speed at the hub height of the wind

turbine in each time step. Second, it uses the hub height wind speed and air density in each time step to

estimate the gross power output of the wind turbine in each time step. Third, it finds the overall mean and

the mean in each month of the gross power output, and multiplies this by the overall loss factor to calculate the mean net power output for each month and for the entire data set. Fourth and finally, it

multiplies the mean net power output by the number of hours in a year (8,760) to find the annual mean

net energy production, and it similarly multiplies the monthly mean net power outputs by the number of hours in each month to find the monthly mean net energy production.

Determining the wind speed at hub height

If the data set contains wind speeds measured at the turbine hub height, Windographer will simply use

those wind speeds. Otherwise, it will synthesize wind speed data at the hub height for each time step in the data set. In doing so, Windographer solves for the best-fit power law profile in each time step and

uses that profile to extrapolate (or interpolate) to the hub height. For greater control of this process, use

the Virtual Anemometer window to synthesize the wind speed at hub height before calculating the output of the wind turbine.

Estimating turbine power output in each time step

Windographer assumes that the gross turbine power output (before accounting for losses) depends on

three factors: the turbine's power curve, the wind speed at hub height, and the air density. To calculate

the gross power output in a particular time step, Windographer first chooses the appropriate power curve. Often the wind turbine properties comprise only one power curve, in which case no choice must be made.

But if the turbine properties comprise multiple power curves, each measured at a different air density,

Windographer chooses the power curve whose corresponding air density is closest to the air density in the current time step. The article on air density explains how Windographer calculates the air density in each

time step. The article on the Create New Wind Turbine window explains how to specify multiple power

curves for different air densities.

Once it has chosen the appropriate power curve, Windographer calculates the power output by referring to

the power curve and performing an air density correction to account for any difference between the actual air density and the air density at which the power curve applies. Windographer performs this air density

correction according to the recommendations of IEC standard 61400-12-1 (2005). The nature of this

air density correction depends on the power regulation method that the wind turbine uses.

For a stall-controlled wind turbine, Windographer first calculates the power output predicted by the power curve for the measured wind speed, then adjusts the power output according to the following equation:

Calculating the Energy Output of a Wind Turbine

where:

P0 is the power output predicted by the power curve for the wind speed in the current time step [kW]

is the actual air density in the current time step [kg/m³]

For a pitch-controlled wind turbine, Windographer first calculates the 'effective wind speed' resulting from

the current air density, then refers to the power curve to find the power output predicted at that effective

wind speed. The following equation gives the effective wind speed:

Calculating mean net power output

Once Windographer has calculated the gross power output of the wind turbine in each time step, it

calculates the mean gross power output for each month and for the data set as a whole, then calculates

the mean net power output using the following equation:

Calculating mean net energy output

Once Windographer has calculated the mean net power output for each month of the year and for the

entire data set, it multiplies these values by the appropriate number of hours to find the turbine's mean

net energy output annually and by month. For example, to calculate the mean annual net energy output, Windographer uses the following equation:

Similarly, Windographer calculates the average net energy output for each month using the following

equations:

ρρ0 is the air density at which the power curve applies [kg/m³]

where:

U0 is the actual wind speed recorded in the current time step [m/s]

ρ is the actual air density in the current time step [kg/m³]

ρ0 is the air density at which the power curve applies [kg/m³]

where:

is the mean gross power output, before losses [kW]

foverall is the overall loss factor

where:

is the mean net power output over the entire data set [kW]

8760 is the number of hours in a (non-leap) year

and so on,

Tip: For a more accurate estimate of wind turbine output, clean up any problems

in your data set with Quality Control window before performing the calculations in the Wind Turbine Output window.

See also

Air density

Overall loss factor

Power regulation method

Create New Wind Turbine window


Virtual Anemometer window




where:

is the mean January net power output [kW]

744 is the number of hours in January

is the mean February net power output [kW]

672 is the number of hours in a non-leap February

The following examples illustrate the use of the Quality Control window. The data sets that appear in the images below, one from

Napoleon, North Dakota and another from Raynesford, Montana in the United States, came from the University of North Dakota's

Energy and Environmental Research Center website www.undeerc.org/wind/winddb/default.asp in January 2007. Though the

data sets are real, our analyses are purely hypothetical, and serve only to illustrate the kind of analysis you could perform with the

Quality Control window.

In the first example, we created a search rule to find anomalies in the wind direction data that might have been the result of direction

sensor icing. The search rule applied when the temperature was below freezing and the 40 metre direction varied less than six degrees

over a four-hour period.

After searching the data set, Windographer displayed in the Search results table a numbered list of all items that met the search

criteria. Item 2 in the Search results table below reports a segment of 223 time steps that meets the search criteria:

Quality Control Examples

In the legend of the time series graph, we checked Direction 40m and Temperature to display those columns in the graph.

Tip: Check one of the leftmost checkboxes to display the variable in the top graph, or one of the

rightmost checkboxes to display it in the bottom graph. The top graph does not appear if you check none

of the leftmost checkboxes.

The time series graph shows that the 40 metre direction sensor appears to have frozen in place just before midnight on October 16

when the temperature dropped below freezing and remained frozen until midday on October 18 when the temperature rose above

freezing.

To find out whether the wind speed sensors may also have frozen, we created a new search rule. This time we searched for times

when all three speed sensors showed measurements of less than one metre per second over a two-hour period. Because we had

already created a search rule to identify wind direction sensor icing, we based our new search rule on a copy of the first one.

After changing the name of the rule to 'All speed sensor icing' and clicking OK, we clicked Search Data Set to display a list of all items

meeting the search criteria for both search rules in the Search results table.

Windographer displayed the two rules in the Search rules table along with their identifying colors. Item 3 in the Search results table

met the criteria for the All speed sensor icing search rule and appeared with a blue highlight in the time series graph. The time series

data show a period of negative wind shear before all three speed sensors appear to freeze before midnight on October 17. At

midday on October 18, all three speed sensors appear to become unstuck.

In the previous examples, we defined search criteria to compare values in data columns to single values, searching for wind direction

values varying less than six degrees, temperature values less than zero degrees Celsius, or wind speed values less than one metre per

second. In the final example we defined search criteria to compare data columns to each other to find occurrences of negative wind

shear. We only considered times when the wind speeds decreased with height across all three anemometers over four hours.

By examining the following time series graph for Item 5, we observed that negative wind shear events tend to coincide with winds

from the west, between about 260 degrees and 275 degrees. We might believe this pattern to be legitimate if the terrain rose steeply

to the west of the measurement station, therefore occasionally causing negative wind shear due to cold air drainage. A quick look at

the topographic map, however, shows that the terrain surrounding the measurement station is quite flat and even slopes downward to

the west. That combined with the fact that many of the negative wind shear events, including this one, occur just before or after an

apparent icing event, suggest that the negative shear events are simply artifacts of wind speed sensor malfunctions due to icing.

Because this data segment appears to be unreliable, deleting it or replacing it with synthesized data would improve the quality of the

data set.

To delete a data segment, select it by clicking and dragging on the graph, then click the button labeled Delete. For a full description of

deleting, replacing, and restoring data segments, please see the article on the Quality Control window.

See also

Wind shear

Negative wind shear

Quality Control Window




� IEC Standard 61400-1 2nd Edition (1999) 'Wind turbine generator systems – Part 1: Safety

requirements'

� IEC Standard 61400-1 3rd Edition (2005) 'Wind turbine generator systems – Part 1: Design

requirements'

� IEC Standard 61400-12-1 1st Edition (2005) 'Wind turbines – Part 12-1: Power performance

measurements of electricity producing wind turbines'

� Manwell JF, McGowan JG, and Rogers AL (2002) Wind Energy Explained, John Wiley & Sons

Limited.

� Stevens MJM, Smulders PT (1979) 'The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes', Wind Engineering, 3, 132-145

� Wind Energy Resource Atlas of the United States at http://rredc.nrel.gov/wind/pubs/atlas/tables/A-8T.html

� Wind Energy Reference Manual at www.windpower.org (last accessed April 30, 2008)




References

tutorial windografher

Documents