analysis and reporting of i-v curve data from large pv...
TRANSCRIPT
Solmetric Webinar
January 16, 2014
Paul Hernday
Senior Applications Engineer
cell 707-217-3094
Analysis and Reporting of I-V Curve
Data from Large PV Arrays
http://www.freesolarposters.com/too
ls/poster?lead=www.solmetric.com
The Goals of I-V Data Analysis
External Goals
• Satisfy the terms of the contract
• Delight your client
Internal Goals
• Identify performance issues early and resolve them efficiently
• Use the learnings to optimize your business processes
• Develop core competencies around PV array performance analysis.
• Deliver maximum impact for your measurement dollars
The Focus of I-V Data Analysis Reveal the real hardware performance
• Real hardware performance can
be clouded by issues related to
weather, obstructions, or
measurement technique.
• Issues generally require follow-up.
In some cases re-testing, or repair
and retesting, may be needed
• In other cases just identifying the
cause may be sufficient (for
example, inter-row shading from
making the measurements early in
the day).
• In some cases the follow-up may be
to simply watch the performance for
potential degradation over time.
Measurement Issues • Irradiance sensor not in POA
• Thermocouple not attached
• Thermocouple location
• Resistive losses
Actual array
performance
Weather Issues • Low irradiance
• Variable irradiance
• Wind
Obstruction
Issues • Shade
• Soiling
Hmm…
Topics
• PV Analyzer operation
• PV principles useful for data analysis
• Using the I-V Data Analysis Tool
• Interpreting your results
• Creating a summary
• Measurement tips
• Provides a much more complete picture of PV array performance, in much less time, than separate current and voltage measurements.
• Array performance can be measured and issues resolved even before the inverter arrives.
PVA1000 PV Analyzer & SolSensor
• Full I-V curve for maximum detail
• ± ½% accuracy for I and V
• 20A, 1000V ranges
• Wireless interconnection
• 100m sensor range
SolSensor Wireless PV Reference Sensor
• Replaces the Wireless Sensor Kit
• Measures irradiance, temperature, tilt
• Self-aligns in plane of array. Clamp provided.
• 100 meter wireless range (open line of sight)
• Integrated design (sensors, rechargeable battery, common wireless unit)
• Uses same wireless USB adapter as the I-V measurement unit
SolSensor will also be available as an
upgrade to the PVA-600. The upgrade
will require the PVA to be returned to
factory for modifications. Price and exact
timing are not yet established.
Built-in PV models
Module make & model Azimuth
Irradiance Module temperature
Tilt Latitude
Longitude Date & time
3 dots predict curve shape
All wireless
How It Works
PV Module or string
Irradiance Temperature Tilt
I-V data
The Measured I-V Curve from the curve tracer
Curr
ent
Voltage
Isc
Voc
• Actual I-V curve.
• No adjustments for
irradiance or temperature.
• Not affected by your
performance model.
The Predicted I-V Curve from the PV model
Curr
ent
Voltage
Isc
Voc
Imp, Vmp
Expected I-V curve shape,
based on the design details
and the present irradiance
and temperature.
Measurement vs. Prediction the bottom line
Curr
ent
Voltage
Isc
Voc
Imp, Vmp
“Performance Factor” is 100% if
measured max power value agrees
with the prediction of the PV model.
Typical Measurement Setup
Courtesy of Chevron Energy Solutions © 2011
Typical Measurement Setup
PC running PVA software
Test Process Example: Measuring strings at a combiner box
Hardware setup (do once at each combiner box)
1. Mount SolSensor to PV module and attach thermocouple
2. Open the combiner DC disconnect
3. Lift the string fuses
4. Clip PVA test leads to the combiner buss bars
1. Insert a string fuse
2. Press “Measure”
3. View and save results
4. Lift the fuse
Electrical measurement (repeat for each string)
15 seconds per string, typically
Taking and Saving a Measurement
2
3
1
Completed Measurement
Exporting I-V Curve Data
Exported Data
• The PVA software automatically creates this
data directory tree on your hard drive (you
select the location).
• The I-V Data Analysis Tool (DAT) accesses
data from this tree.
• Each string folder contains a csv file of your
string measurement.
• If you also measured the modules that
make up the string, there will be module-
level folders within the string folders.
• The DAT can import at the level of a single
inverter or all inverters (entire system).
The ‘Project’ File
• Contains your PV model and I-V measurement data
• Easy to share between offices, and with Solmetric for
technical and applications support.
xxxxxx.pvapx (v3.x)
xxxxxx.pvap (v2.x)
Topics
• PV Analyzer operation
• PV principles useful for data analysis
• Using the I-V Data Analysis Tool
• Interpreting your results
• Creating a summary
• Measurement tips
I-V and P-V Curves Expect this shape for healthy cells, modules, strings, arrays
Cu
rre
nt
Voltage
Isc
Voc
I-V curve
Vmp
Imp
Po
we
r
P-V curve
Pmax
• The P-V (power vs. voltage) curve is calculated from the measured I-V curve
• Both curves auto-scale, so the relative heights of the curves is not important.
Irradiance Effects Conventional crystalline silicon module
0 5 10 15 20 25 30 35
9
8
7
6
5
4
3
2
1
0
Voltage (V)
Curr
ent
(A)
1000 W/m2
800
600
• Isc doubles when irradiance
doubles, but this rule does not apply
at all points along the curve.
• Below 400 W/m2, and especially
below 200, cell voltages drop
significantly.
• Low-light measurements do not
accurately predict performance at
high irradiance! That’s true of ANY
performance testing method, not
just curve tracing.
See a great demo of I-V curve vs irradiance at:
http://www.pveducation.org/pvcdrom/solar-cell-
operation/effect-of-light-intensity
Temperature Effects Conventional crystalline silicon module
• Vmp and Voc drop 0.35 -
0.45 %/C.
• Smaller effect for irradiance,
but still important.
• The PV model accounts for
these temperature effects
• The modeling is more
accurate if the temperature
measurement is accurate
• Temperature affects voltage
more strongly than the
current
0 5 10 15 20 25 30 35
9
8
7
6
5
4
3
2
1
0
0C
25
50
Voltage (V)
Curr
ent
(A)
‘Square-ness’ of the I-V Curve
• An important figure of merit of a
PV source is the square-ness of
its I-V curve.
• Squarer means higher Pmax for a
given Isc and Voc.
• In an ideal world, the curve would
be perfectly square and output
power would be Isc x Voc. But
this is not physically possible.
Isc
Voc
Curr
ent
Voltage
Increased square-ness
means increased Pmax
Isc
Voc
Fill Factor A measure of the square-ness of the I-V curve
Curr
ent
Voltage
Fill Factor = = Area of green rectangle
Area of blue rectangle
Current ratio Imp/Isc
Voltage ratio Vmp/Voc
Imp
Vmp
Max Power Point
Imp x Vmp (watts)
Isc x Voc (watts)
For xSi, the Fill Factor is normally > 0.7
Fill factor dependence of bilinear I–V curve
translation accuracy, IEEE, Skoczek, 2008.pdf
Normalized I-V curves of field tested devices
• Currents and voltages have been
‘normalized’ to max values of 1.0
• Blue I-V curves are from new PV
modules.
• Red curves are from 20-year field-
aged PV modules.
• Fill Factor is an important
troubleshooting tool because:
• Higher Fill Factor means higher
output power
• Fill Factor is less dependent on
irradiance than Pmax or Isc, so
it allows us to compare string
measurements even if
irradiance is varying
Fill Factors of Field-Aged PV Modules
Stringing Modules for Higher Voltage
• Voltages add at each level of
current.
• Location of building blocks in I-V
graph does not correspond to
locations of modules in the string!
I-V
Building
Blocks
Series
Cu
rre
nt
Voltage
Paralleling Modules for Higher Current
I-V
Building
Blocks
Cu
rre
nt
Voltage
• Currents add at each level of
voltage.
• Location of building blocks in I-V
graph does not correspond to
locations of modules in the string!
Series/Parallel Combination C
urr
en
t
Voltage
• Array I-V curve will be smooth
and regular if the modules have
matching currents and voltages.
• Location of building blocks in I-V
graph does not correspond to
locations of modules in array.
• If we solidly shade any module in
the array, we lose the upper right
building block and the total I-V
curve will have a step in its place.
More on this topic later.
Deviations from Normal I-V Curve Each will be explained later in the webinar
Conventional measurements do
not reveal many of these effects.
I-V Curve of a Partially Shaded String
• Shade causes steps in the I-V curve, in turn causing multiple peaks in the P-V curve.
• Changing conditions cause peaks to move around
• Inverter’s job is to minimize disruption to the whole system.
Cu
rre
nt
Voltage
Isc
Voc
Po
we
r
Bypass diode turns on
here. This is the point at
which the weak (or
shaded) has reached it’s
maximum possible
current.
The Purpose of Bypass Diodes
The primary purpose of bypass diodes is
to prevent damage to the PV module and
supporting structure under mismatch
conditions (eg shade, soiling…)
In doing so, they also mitigate the impact
of shade on energy production.
Current Flow in Normal Operation
+
Cell group
Cell group
Cell group
Bypass
Diodes
Bypass diode turns on when the shaded cell(s) can
no longer pass as much current as the non-shaded
cells.
Current Flow with One Shaded Cell
+
Cell group
Cell group
Cell group
Bypass
Diodes
Bypass diodes start turning on when the shaded cell(s) can
no longer pass as much current as the non-shaded cells.
Rules of Thumb For shading, soiling, or other current mismatch effects
• A bypass diode turns on when the most shaded cell in its cell group
can no longer ‘keep up’ with the rest of the module or string.
• The depth of the current step in the I-V curve tells us how heavily
the most shaded (or soiled) cell is obstructed.
• The width of the current step tells us how many cell groups are
obstructed
• The location of the current step in the I-V curve does not tell us
where the shading is located in the string under test. The deepest
steps always appear at the higher voltages (the right-hand region of
the I-V curve), regardless of where the obstruction is in the array.
Array With Full-Shaded Module C
urr
en
t
Voltage
• The shaded module’s bypass
diodes turn on, removing its
voltage (and power) from
production.
• The result is seen in the upper
right building block, causing a step
in the I-V curve.
Array With Full-Shaded Cell Group
Series
Cu
rre
nt
Voltage
• The narrowest steps occur when a
single cell group is shaded or its
bypass diode fails short.
• In this example, we shade one of a
modules three cell groups.
String with Shaded Cell Group
• The height of the step is related to the
shading factor of the most shaded cell
in the cell group.
• In this example, we shade one entire
module with 33% shade cloth,
reducing the irradiance to 2/3 of the
level seen by the rest of the array.
Series
Cu
rre
nt
Voltage
Expected max power point
Visualizing Current Mismatch Effects Curr
ent
(A)
Voltage (V)
Isc
Voc
Normal I-V curve
When a cell group can no
longer sustain the current
of the other cell groups, its
bypass diode turns on (at
the X’s), allowing string
current to increase. This
gives the mismatch I-V
curve its roller coaster
shape.
Shaded
cell
groups
Un-
shaded
cell
group
X
X
Topics
• PV Analyzer operation
• PV principles useful for data analysis
• Using the I-V Data Analysis Tool
• Interpreting your results
• Creating a summary
• Measurement tips
Overview of Data Analysis & Reporting
1. Export data from PVA software. This exports the most recent
measurement for each location in the array tree.
2. Open the Data Analysis Tool
3. Use the DAT controls to import and automatically crunch the numbers:
1. Import data
2. Create string table & histograms
3. Create ‘Measured vs. Modeled’ table
4. Plot I-V curves (overlaid at the combiner level)
5. Translate Isc, Imp, Vmp, Voc to STC if desired
4. Review and interpret data
5. Generate punch list if needed
6. Generate reports of the DAT results and your interpretation (optional)
Overview of the I-V Data Analysis Tool
I-V Data Analaysis Tool Controls tab
Outputs of the DAT
1950
2000
2050
2100
7
6
5
4
3
2
1
0
Fre
qu
en
cy
Pmax (Watts)
7
6
5
4
3
2
1
0
Cu
rren
t (A
mp
s)
0 100 200 300 400 500
Voltage (Volts)
7
6
5
4
3
2
1
0
Cu
rren
t (A
mp
s)
0 100 200 300 400 500
Voltage (Volts)
String Table I-V Graphs (overlay)
Histograms
• These displays are generated automatically.
• Used together they make quick work of
interpreting I-V curve data.
• Automatically create a professional report
including any or all of these displays.
String Table
Limits
(user settable)
Statistics
(per column)
Parameter
values
(per string)
95.44%
99.74%
68.26%
+1 sd +2 sd +3 sd -3 sd -2 sd -1 sd Mean or
“average”
value
Standard Deviation
The Standard Deviation
describes how widely your
data is spread out.
It’s easiest to visualize
with a normal or bell-
shaped distribution of
data, as shown here.
Even if the distribution is not
bell shaped, standard
deviation is still an important
measure of spread.
Histograms Graphical displays of how data values are distributed
http://www.mathsisfun.com/data/histograms.html
Parameter value
Co
un
t Bin (or ‘bucket’)
(5 wide in this histogram)
Counts are
whole
numbers
1 2
5
Example:
Histogram
of 99 data
values
25
20
15
10
5
0 50 60 70 80 90 100 110
Common Histogram Shapes
http://asq.org/learn-about-quality/data-collection-analysis-tools/overview/histogram2.html
Normal or
bell-shaped
Right-skewed
Double-peak
Plateau
Fill Factor of healthy PV strings
Isc values measured over a long day
Voc of strings measured on a cold morning
and a hot afternoon
Examples:
Fill Factor of randomly soiled strings
http://asq.org/learn-about-quality/data-collection-analysis-tools/overview/histogram2.html
Outliers
Any type of distribution can have outliers. These are
points that just don’t fit the distribution of the rest of
the population of points.
Here’s an example of low-side and high-side outliers
of a bell shaped distribution:
An important element any data analysis is identifying
outlier strings and sorting through the possible
causes.
Using the Data Analysis Tool
1. Selecting Which Sensor Data to Import
This slide needs work
given the new
definition of features
1. Select Which Sensor Data to Import
2. Browse for Your I-V Data Tree (exported from the PVA software)
Inverter5
Inverter1
Inverter2
Inverter3
Inverter4
System
Exported PVA data
Washington High School
Combiner1
Combiner2
2. Browse for Your I-V Data Tree (exported from the PVA software)
Select the desired level.
All data below that level
will be imported to the
Data Analysis Tool.
3. Import and Analyze the Data
3. Import and Analyze the Data
1950
2000
2050
2100
7
6
5
4
3
2
1
0
Fre
qu
en
cy
Pmax (Watts)
Samples of the Table and Histogram worksheets of the DAT
4. Compare Measured vs. Modeled Values
Home
File Path
Measured Model Measured Model Measured Model Measured Model
Combiner1\String1\String1 10-9-2013 02-01 PM.csv 6.09 6.17 5.63 5.74 354.8 369.6 449.0 458.8
Combiner1\String10\String10 10-9-2013 02-04 PM.csv 7.78 7.73 7.07 7.18 346.6 366.2 446.2 462.5
Combiner1\String11\String11 10-9-2013 02-05 PM.csv 6.96 6.85 6.37 6.37 348.5 369.9 445.1 462.2
Combiner1\String12\String12 10-9-2013 02-05 PM.csv 6.56 6.64 6.00 6.18 350.3 370.4 445.8 461.7
Combiner1\String13\String13 10-9-2013 02-05 PM.csv 5.97 6.25 5.43 5.82 353.9 371.3 445.1 460.8
Combiner1\String14\String14 10-9-2013 02-06 PM.csv 6.75 6.85 6.08 6.37 356.1 370.0 450.9 462.3
Combiner1\String15\String15 10-9-2013 02-06 PM.csv 6.92 7.07 6.35 6.57 357.6 370.4 453.5 463.6
Combiner1\String16\String16 10-9-2013 02-06 PM.csv 6.69 6.87 6.15 6.39 354.8 371.7 451.6 464.0
Combiner1\String17\String17 10-9-2013 02-07 PM.csv 7.22 7.50 6.61 6.97 354.3 370.4 453.2 465.5
Combiner1\String18\String18 10-9-2013 02-08 PM.csv 7.18 7.56 6.52 7.03 354.8 371.1 452.4 466.6
Combiner1\String19\String19 10-9-2013 02-08 PM.csv 7.20 7.25 6.61 6.74 353.2 371.7 452.1 465.7
Combiner1\String2\String2 10-9-2013 02-02 PM.csv 6.67 6.74 6.13 6.27 352.6 368.5 449.7 460.3
Combiner1\String20\String20 10-9-2013 02-08 PM.csv 7.16 7.38 6.58 6.86 354.0 370.6 453.0 465.3
Combiner1\String21\String21 10-9-2013 02-09 PM.csv 7.47 7.52 6.89 6.99 355.8 370.2 455.4 465.5
Isc (Amps) Imp (Amps) Vmp (Volts) Voc (Volts)
4. Compare Measured vs. Modeled Values
Sample of the Model worksheet of the DAT
5. Select Data for I-V Curve Graphs
Usually we want
to plot the entire
population of data
6. Plot I-V Curves
6. Plot I-V Curves
Sample of an I-V Curves worksheet of the DAT
7. Generate Report
Topics
• PV Analyzer operation
• PV principles useful for data analysis
• Using the I-V Data Analysis Tool
• Interpreting your results
• Creating a summary
• Measurement tips
Starting Points for Interpreting I-V Data
I-V Curve Graphs
• Scan for outliers and identify those strings
(hover with cursor)
Histograms
• Scan for outliers and odd shapes
• Correlate shapes with variability of
irradiance and temperature
Table
• Check the statistics (rows 5-9)
• Enter limit values (blue fields)
to identify outliers (shaded yellow)
The starting point for your analysis is a matter of personal preference, but if
you like your information in graphical form, this is a good flow.
Standards for Pass/Fail
Common standards:
a. Consistency of values across the population of strings (outliers?)
b. High values of Performance Factor, or
c. Agreement of translated curves with STC-based model
Indications of good performance:
1. Clean I-V curves
2. Performance Factor values above 90%
3. Fill Factor values > 0.7
4. Current ratio values > 0.9
5. Voltage ratio values > 0.78
• High irradiance is assumed.
• Limit values vary by module
technology and manufacturer.
Deviations from Normal I-V Curve
Conventional measurements do
not reveal many of these effects.
Steps in the I-V Curve
Steps in the I-V Curve Typically caused by shade, soiling, debris, snow, or cracked cells
The small steps represent shaded cell groups within modules.
The width of the step tells us how many cell groups are involved.
The height of the step tells us about the extent of shading on the most shaded cell in the group; lower amps means it’s more shaded.
We can’t tell from the I-V curve where the shaded cell groups are located in the string.
Record the string ID (for example i3c4s7) for the punch list and/or report.
350 Clark i1c3
Partially shaded residential array
Approximately 40% reduction in string’s output power
Partially shaded residential array
Hockey Sticks
Hockey sticks often represent systematic shading over several adjacent cell groups or modules.
In this case, the low current value of the hockey stick steps suggests that at least one cell in each of the cell groups is almost completely shaded.
This type of pattern is unlikely to be caused by soiling or scattered shade because of the extent and uniformity of the obstruction and the fact that it happens on only a few of the strings.
Random Non-uniform Soiling Seagull example
• Effect similar to partial shading
• Steps in the I-V curve
• Smallest steps correspond to
individual cell groups
Light Snow Cover on Array
Heavier Snow Cover on Array
Low Isc
Uniform soiling and dirt dams can
both reduce Isc without causing steps
in the I-V curve.
This array had both types. Curves
measured before and after cleaning
showed that each caused 50% of the
measured drop in string performance.
Low Current Due to Soiling Uniform soiling and dirt dams are common causes
Uniform soiling Dirt dam
Low Voc
In this set of curves from
a combiner box, the
shapes and levels are
very consistent.
Most likely, the irradiance
and temperature were
stable throughout and the
strings were quite
uniform.
Normal Variations in Voc
In this set of curves from
another combiner box,
the shapes are mostly
consistent but the
voltages are slightly
spread - why? Here are
several possibilities:
1.Strings are slightly
mismatched in voltage
2.Temperature is rapidly
changing due to wind
or shifting clouds
3.The strings don’t all get
the same amount of
ventilation behind the
modules.
4.Voc changes at low
irradiance, but that
doesn’t fit this situation.
Normal Variations in Voc
Possible Shorted Bypass Diodes
If Voc is shifted downward by approximately a module Voc/N it may indicate a dropped cell group, likely caused by a shorted bypass diode.
In this example at least two strings are likely to have one or more dropped cell groups.
Validate dropped cell group by comparing the apparent Voc in the I-V curve with the true Voc value in the Table tab.
Full shading of a PV cell causes a similar looking left-shift, but a ‘tail’ is usually present where curve approaches x-axis.
FW Solar Field
Voc Histogram
Low Voc vs. “Last Point” Effect
s13
Voc
513
s12
s14
Voc
512
513 s11
Voc
498 Others
(Avg)
Voc
510
The green trace’s Voc value is about
12 volts lower than the average of
the other strings. This is likely
caused by a shorted bypass diode.
The blue and orange traces (s12,13)
do not reach all the way down to the
x-axis. This is because the 100 I-V
points were ‘used up’ before the
curve reached zero current. This
sometimes happens when Isc is very
low or there is a low- current ‘tail’ on
the curve, as shown here.
If the curve does not reach the x-
axis, look at the table value of Voc,
which is from a Voc measurement
performed immediately before the I-V
curve is measured.
Potential Induced Degradation
PID is driven by high voltage stress. It’s more likely to occur at higher voltages and negative polarity, and in modules with less effective encapsulation.
Electro-corrosion type is not reversible.
Symptoms include reduced Voc and Fill Factor (more rounded knee). Can be seen at string or module levels.
South string, west modules
Fill Factor Histogram
Rounder Knee
Rounder Knee
A rounder knee is difficult to differentiate from changes of
slope in the horizontal and vertical legs of the curve.
Reduced Slope in Vertical Leg
0
1
2
3
4
5
6
7
8
0 50 100 150 200 250 300 350 400
Voltage - V
Cu
rren
t -
A
String 4B14
String 4B15
Increased Series Resistance Reduced slope in vertical leg of curve
Neighboring
strings
Failed
module
Increased Slope in Horizontal Leg
Image courtesy of:
http://www.pveducation.org/pvcdrom/solar-cell-
operation/effect-of-light-intensity
The normal slope in the horizontal
leg of the I-V curve is caused by
shunt resistance in the PV cells.
Shunt resistance allows a small
current to flow backward through the
cells, and the level of that current is
proportional to the cell voltage, giving
that leg of the curve its familiar linear
downward slope.
Over time it is possible for cells to
degrade to lower levels of shunt
resistance, which increases the slope
in the horizontal leg.
Increased Slope in Horizontal Leg Shunt resistance
Increased Slope in Horizontal Leg Tapered shading or soiling
350 Clark i2c3
Typically caused by tapered shading or tapered soiling.
For a uniform slope, each cell group must be obstructed to a slightly different extent. Often slight steps will remain.
Common causes are inter-row shading early or late in the day, or dirt dams that get progressively wider across a string of modules in portrait mode.
Electrical shunts can cause slopes, but it’s much less common. PID can also cause the slope, and may be accompanied by low Voc.
PID is driven by high voltage stress. It’s more likely to occur at higher voltages and negative polarity, and in modules with less effective encapsulation.
Electro-corrosion type is not reversible.
Symptoms include reduced Voc, rounder knee, and increased slope in the horizontal leg of the curve. Can be seen at string or module levels.
Increased Slope in Horizontal Leg Potential Induced Degradation
Fill Factor Representation of steps and slopes in the curve
350 Clark i2c3
The stepped and sloped I-V curves are represented as low- side outliers in the Fill Factor histogram.
Fill Factor is a good diagnostic tool because it is not strongly affected by level of irradiance.
Pmax
Isc
Strongly Irradiance-Dependent Parameters These tend to have irradiance-like distributions unless blurred by other issues
350 Clark i3
Irradiance
Imp
Histograms of the same population of measurements
350 Clark i3
Shade effects
Shade effects
Less Irradiance-Dependent Parameters (At high light levels. At low light levels their dependence increases.)
Shade effects
Irradiance Fill Factor
Performance
Factor
Voc
Histograms of the same population of measurements
Somewhat dependent
due to heating at high
irradiance.
Creating Custom Graphs Easiest to do in the Table worksheet
Limitations of STC Translation Not unique to curve tracing!
• Traditionally, translation or normalization of I-V data to STC conditions is
much less accurate if the curves were measured at low light conditions,
especially at <400W/m2 . The PV model used in PVA-1000 with SolSensor
improves this situation by modeling low light effects; this correction is made
automatically if the low light modeling data is available in the database.
• If irradiance is unstable, there will be more ± scatter in the translation. (This
is minimized by the PVA-1000 with SolSensor because the I-V curve and
sensor measurements are triggered simultaneously.)
• Worst case is when irradiance is low and unstable
• Measured temperature may not track the strings under test (inconsistent
placement of thermocouples; sub-arrays with different temperature profiles;
variable wind).
Topics
• PV Analyzer operation
• PV principles useful for data analysis
• Using the I-V Data Analysis Tool
• Interpreting your results
• Creating a summary
• Measurement tips
Creating a Summary of your data analysis in MS Excel
• Select “Deviation” and “Follow-up” items from drop-down lists, or enter your own text
• Data filtering allows sorting for particular cases
• Can send the worksheet to a printer or PDF file
Editable Drop-down Lists
Summarizing the Histograms in MS Excel
I-V Curve Graph Worksheets
Inverter Combiner String(s) Comment
Histogram Worksheets
Histogram Comments
PF
FF
etc
Measured vs. Modeled Worksheet
Parameter Comments
Isc
Voc
Imp
Vmp
Table Worksheet
Enter comments here…
Translated Parameters Table (In Table Worksheet)
Enter comments here, if applicable…
Conclusion
Enter comments here…
Creating a Summary of your data analysis in MS Word
Topics
• PV Analyzer operation
• PV principles useful for data analysis
• Using the I-V Data Analysis Tool
• Interpreting your results
• Creating a summary
• Measurement tips
Top 10 Measurement Tips (Many are not unique to curve tracing!)
1. Set your PC clock to the correct local time, time zone, and daylight savings status.
2. Orient the irradiance sensor in the plane of the array.
3. Measure array performance at high irradiance (ideally >700, never less than 400).
4. Avoid mounting the irradiance sensor in shade or strong reflections.
5. In diffuse light conditions, locate the irradiance sensor for an open view of the sky.
6. Remember that the SmartTemp method requires a backside thermocouple.
7. Make sure the thermocouple is in firm contact with the module backside.
8. Place the thermocouple at a location with ‘average’ temperature, and make the
location consistent from sub-array to sub-array.
9. Re-measure the first trace of the session if it has straight line segments.
10. Check for PVA software updates! http://www.solmetric.com/downloads-pva.html
Time Zone Considerations
• The PVA software date/time stamps each measurement.
• The date and time are used in the model to predict the values of the Isc, Imp, Vmp, Voc,
and Performance Factor.
• Before measuring, be sure your PC is set to the correct local date, time, time zone, and
Daylight Savings status.
• Before exporting Project data from PVA software 2.x or 3.0, set your PC’s UTC/GMT offset
to the value that was used when the measurements were actually taken.
• Starting with v3.1, you will not need to fake your time zone before exporting data.
Setting up to make measurements
Exporting Project data
Pacific time
Mountain time
Central time
Eastern time
UTC/GMT Offset (hours)
DST off -8 -7 -6 -5
DST on -7 -6 -5 -4
GMT Offset, Time Zone, DLS
Check WWW.timetemperature.com to look up the time
zone and Daylight Savings details for your site.
The ‘First Trace’ Effect The PVA uses the first trace to optimize internal settings
• The PVA software uses the first trace
to ‘learn’ the voltage and current
characteristics of the PV source.
• The PVA then selects internal circuit
settings to optimize the
measurement of that type of device.
• If you get a first trace that has long
straight line segments, that’s the
‘learning’ trace. Just take the
measurement over.
• All subsequent measurements will
use those optimized internal settings.
• If the type of device you are
measuring changes in mid-session,
you may see the ‘first trace’ effect
again, and need to take that first
measurement over.
• High irradiance
– Ideally more than 700 W/m2, and not lower than 400 W/m2 .
– The I-V curve changes shape at low light, making it a less useful predictor of performance at high irradiance.
• 4-5 hour window centered on solar noon
– For good irradiance level and reduced angle of incidence effects
– http://www.esrl.noaa.gov/gmd/grad/solcalc/
• Low or no wind
– For more consistent module temperature measurements
– Width of I-V curve varies inversely with temperature
Recommended Weather Conditions For Array Performance Testing
Good conditions more meaningful data
Effects of Unstable Irradiance & Temp.
• Unstable irradiance introduces
‘scatter’ in the predicted
performance values, especially if
there is a time delay between I-V
and irradiance measurements.
• The greater the time delay, the
greater the scatter.
• The steeper the irradiance ramp,
the greater the scatter.
• The same thing happens with
temperature measurement, but
to lesser degree because
temperature ramping is slower,
and the dependence of
performance on temperature is
weaker.
Temperature Profile – Flush Mounted Array
Photo courtesy of Sun Lion Energy Systems
Consistency of Thermocouple Location Choose a good location and repeat it on each sub-array
Products Available from Solmetric
Megger®
MIT-430
Insulation
Tester
SunEye 210
Shade Tool
FLIR®
Infrared Cameras
PV Designer Software PV Analyzer
I-V Curve Tracers
Solmetric Webinar
January 16, 2014
Paul Hernday
Senior Applications Engineer
cell 707-217-3094
Analysis and Reporting of I-V Curve
Data from Large PV Arrays
http://www.freesolarposters.com/too
ls/poster?lead=www.solmetric.com