uncertainty of virtual met mast measurements in dk · pdf fileuncertainty of virtual met mast...

28 NOVEMBER 2014

VINDKRAFTNET MEETING - COWI PRESENTATION 1

Uncertainty of Virtual Met Mast Measurements in DK

Torkel D. Løland & Jasmin Bejdić, 1905 Wind and M&E

Agenda

28 NOVEMBER 2014


› Introduction

› Method

› Measurement and validation uncertainties

› Transfer of uncertainty from validation mast positions to project sites

› Wind direction uncertainty

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Introduction

› Background

› Tendering of 350 MW offshore wind capacity at six nearshore sites.

› It has been decided by the Danish Energy Agency that no offshore met masts shall be installed.

› COWI contracted by Energinet.dk who is responsible for performing the pre-investigation for the six nearshore sites.

28 NOVEMBER 2014


Introduction

› Motivation

› Using virtual met masts from a mesoscale model instead of offshore met masts/lidars to provide site specific wind data with a corresponding uncertainty for wind resource estimations at six nearshore wind farms.

FINO 1, Alpha Ventus wind farm. Source: Martina Nolte.

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Introduction

› Project

"Six nearshore wind farms, study related to wind resource"

1) Validation of StormGeo's WRF based mesoscale model.

2) Estimation of wind resources and corresponding uncertainty at six nearshore wind farm sites by use of virtual met mast measurements.

3) Project Findings have been subject to a third party validation by DEWI

Method

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› Validation approach

Mesoscale model simulations performed over two calendar years with a spatial resolution of 1 km.

Simulation period: 01-09-2011 to 31-08-2013.

Method

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› Validation approach

Identifying relevant met masts for mesoscale model validation.

Gathering met mast data and available met mast information.

Filtering data for errors, tower shadow effect.

Data analysis and uncertainty estimation.

Method

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› Comparison of mesoscale data and met mast data at validation masts

Calculation of mean wind speed bias, comparison of frequency distributions, correlation analysis, wind rose comparison, wind shear comparison, comparison of Weibull A and k parameters.

Method

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› Example where mesoscale model performs very well.

Høvsøre

Mean wind speed bias: -0.1 m/s in 116 m. 1 % model underestimation compared to observed mean wind speed.

0 5 10 15 20 25 30 350

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Wind Speed [m/s]

Cum

ula

tive P

robabili

ty

Cumulative Density Function Høvsøre at 116 m

Observed

Modelled

0 5 10 15 20 25 30 350

5

10

15

20

25

30

35

0 5 10 15 20 25 30 350

5

10

15

20

25

30

35

Observed Wind Speed [m/s]

Modelle

d W

ind S

peed [

m/s

]

Quantile-Quantile Plot [0:0.05:100] - Høvsøre at 116 m

2 4 6 8 10 12 140

20

40

60

80

100

120

140

160

Wind Speed [m/s]

Heig

ht

[m]

Vertical Wind Profile - Modelled vs. Observed at Høvsøre

Modelled, = 0.203

Observed, = 0.182

10%

15%

20%

Wind Rose - Modelled 100m

WEST EAST

SOUTH

NORTH

0 - 5

5 - 10

10 - 15

15 - 20

20 - 25

25 - 30

30 - 35

10%

15%

20%

Wind Rose - Observed 100m

WEST EAST

SOUTH

NORTH

0 - 5

5 - 10

10 - 15

15 - 20

20 - 25

25 - 30

30 - 35

0 5 10 15 20 25 30 350

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

Wind speed [m/s]

Fre

quen

cy o

f oc

curr

ence

Modelled and measured Weibull distributions at Høvsøre at 116 m

A,obs = 11.39, k,obs = 2.4302

A,mod = 11.26, k,mod = 2.3716

Method

28 NOVEMBER 2014


› Example where mesoscale model has some trouble.

Risø

Mean wind speed bias: 0.5 m/s in 125 m.

5.9 % model overestimation compared to observed mean wind speed.

0 5 10 15 20 250

0.02

0.04

0.06

0.08

0.1

0.12

Wind speed [m/s]

Fre

quency o

f occurr

ence

Modelled and measured Weibull distributions at Risø at 125 m

A,obs = 9.07, k,obs = 2.4427

A,mod = 9.63, k,mod = 2.3401

0 5 10 15 20 25 300

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Wind Speed [m/s]

Cum

ula

tive P

robabili

ty

Cumulative Density Function Risø at 125 m

Observed

Modelled

0 5 10 15 20 250

5

10

15

20

25

Wind Speed - Observed [m/s]

Win

d S

peed -

Modelled [

m/s

]

Correlation - MOD versus OBS Risø at 125 m

y = 0.93579x +0.98574

R2 = 0.71383

Data points

1. order linear regression

0 5 10 15 20 25 300

5

10

15

20

25

30

Observed Wind Speed [m/s]

Modelle

d W

ind S

peed [

m/s

]

Quantile-Quantile Plot [0:0.05:100] - Risø at 125 m

2 4 6 8 10 12 140

20

40

60

80

100

120

140

160

Wind Speed [m/s]

Heig

ht

[m]

Vertical Wind Profile - Modelled vs. Observed at Risø

Modelled, = 0.318

Observed, = 0.240

10%

15%

20%


WEST EAST

SOUTH

NORTH

0 - 5

5 - 10

10 - 15

15 - 20

20 - 25

25 - 30

10%

15%

20%


WEST EAST

SOUTH

NORTH

0 - 5

5 - 10

10 - 15

15 - 20

20 - 25

25 - 30

Method

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› Summary of results for validation masts

Mean wind speed bias within the combined uncertainty for most of the validation masts.

For all offshore and coastal met masts biases within or close to actual measurement uncertainty were found.

Validation masts

Wind speed

bias

[%]

Combined

Uncertainty

[%]

Comments

FINO 3 -1.9 3.4 3 km domain

Høvsøre -1.0 2.5 1 km domain

Østerild W 4.0 5.0 1 km domain

Stora Middelgrunden

(Jylland) -3.0 2.9 3 km domain

Stora Middelgrunden

(Sjælland) -1.6 2.9 3 km domain


Risø 5.9 5.2 1 km domain

Omø 2.1 5.0 3 hour resolution,

1 km domain

Horns Rev M2 -0.7 5.0 3 hour resolution,

1 km domain

Quantifying uncertainties at validation positions

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› Quantifying measurement uncertainty at validation masts.

› Quantifying validation uncertainty at validation masts.

› Result: a combined uncertainty estimate at each validation mast position. "Uncertainty of virtual met mast data".


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› Measurement and validation uncertainty

Met Mast Measurement uncertainties [%] Joint

Measurement

Uncertainty

Anemometer Calibration Mast

configuration

Data handling

FINO 2 0.3 1.1 1.5 0 1.9

FINO 3 0.3 1.1 2.2 0 2.5

Stora

Middelgrund

0.3 1.1 1.6 0 1.9

Høvsøre 0.3 1.1 1.0 0 1.5

Risø 0.3 1.1 1.0 0 1.5

Østerild W 0.3 1.1 1.6 1 2.2

Omø 0.3 1.1 1.6 0 1.9

Horns Rev M2 1.1 3.1 1.0 0.5 3.5

Measurement uncertainties

Anemometer Calibration Measurement setup Data handling

Overspeeding caused by sensor dynamics

Steady state calibration Flow distortion from the mast Handling of data

Assymetry of flow incident on anemometer

Possible variation or change in calibration Flow distortion from the boom

Instrumentation system uncertainties including calibration and quantisation effects

Flow distortion from mounting clamps and other protrusions

Flow inclination effects on calibration

Validation uncertainties

Data period

Temporal resolution of data

Sector removal

General modelling uncertainty

Long-term correction uncertainty

Eksempler

The measurement and validation uncertainties are assumed independent from each other. A joint uncertainty can hence be found by taking the square root of the squared sums.

Combined uncertainty at validation mast = 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑚𝑒𝑛𝑡2 + 𝑣𝑎𝑙𝑖𝑑𝑎𝑡𝑖𝑜𝑛2

If mean wind speed bias is lower or equal to the combined uncertainty, the combined uncertainty is assumed equivalent to the virtual met mast uncertainty.


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Measurement uncertainty components Uncertainty limits1

- Anemometer type Min Max FINO2 FINO3 Stora Middelgrund Høvsøre Risø Østerild W Omø Horns Rev M 2

Overspeeding caused by sensor dynamics 0.2 1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.5

Assymetry of flow (shear) incident on anemometer 0.2 2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 1

Joint anemometer uncertainty 0.3 2.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 1.1

- Calibration

Steady state calibration 1 5 1 1 1 1 1 1 1 2.5

Possible variation or change in calibration 0.2 3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 1.5

Instrumentation system uncertainties including calibration and quantisation effects 0.2 1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.5

Flow inclination effect on calibration 0.2 1.5 0.2 0.2 0.2 0.2 0.2 0.2 0.2 1

Joint calibration uncertainty 1.1 6.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 3.1

- Measurement set-up

Flow distortion from the mast 0.5 2 1.5 2 1.5 1 1 1.5 1.5 1

Flow distortion from the boom (no boom/top tube = 0, perfect boom = 0.5) 0 2 0 1 0.5 0 0 0.5 0.5 0

Flow distortion from mounting clamps and other protrusions 0.1 2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

Joint measurement set-up uncertainty 0.5 3.5 1.5 2.2 1.6 1.0 1.0 1.6 1.6 1.0

- Data handling 0 5 0 0 0 0 0 1 0 0.5

Joint data handling uncertainty 0 5 0 0 0 0 0 1 0 0.5

Combined measurement uncertainty 1.2 8.9 1.9 2.5 1.9 1.5 1.5 2.2 1.9 3.5

› Measurement uncertainties

1) Guidelines and recommendations from "Recommended practices for wind turbine testing – 11. Wind speed measurement and use of cup anemometry. 1. Edition"


28 NOVEMBER 2014


› Validation uncertainties

Met Mast

Validation uncertainties [%]

Joint Validation

Uncertainty

Source data period and WRF data temporal

resolution

Sector removal

General modelling

uncertainty

Long-term correction

uncertainty

FINO 2 0 0.4 2 0 2

FINO 3 0 1.1 2 0 2.3

Stora Middelgrund

0 0.7 2 0 2.1

Høvsøre 0 0.4 2 0 2

Risø 0 0.5 5 0 5

Østerild W 4 0.2 2 0 4.5

Omø 0 2.3 4 0 4.7

Horns Rev M2

1 1.7 2 2.2 3.6


28 NOVEMBER 2014


Validation masts

Wind speed

bias

[%]

Combined

Uncertainty

[%]

Comments


Høvsøre -1.0 2.5 1 km domain

Østerild W 4.0 5.0 1 km domain

Stora Middelgrunden

(Jylland) -3.0 2.9 3 km domain

Stora Middelgrunden

(Sjælland) -1.6 2.9 3 km domain


Risø 5.9 5.2 1 km domain

Omø 2.1 5.0 3 hour resolution,

1 km domain

Horns Rev M2 -0.7 5.0 3 hour resolution,

1 km domain

Transferring validation mast uncertainty to project sites

28 NOVEMBER 2014



28 NOVEMBER 2014


› Methodology

› Uncertainty contribution from every validation mast

› Masts of higher relevance is given a higher weighting

› Weighting according to:

1. Mast height

2. Terrain type

3. Source data length and temporal resolution

4. Distance to project site


28 NOVEMBER 2014


› Qualitative assessment

› Base uncertainty obtained from weighting process was subject to a qualitative assessment and scaled according to the project site location.

The transferred uncertainty represents the uncertainty of the generated site-specific wind data.

Uncertainty of wind direction

28 NOVEMBER 2014


› Practical example

"Imaginary" wind farm at the FINO2 position

Fixed layout with 32 WTGs (132 MW)

Applying ±10° and ±20° offset to measured wind direction


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› Practical example

› 5 000 000 kWh "lost" per year.

WD Offset [°]

AEP [MWh]

Park Efficiency [%]

Wake Loss [%]

0 739455.1 94.6 5.4

+10 735976.0 94.4 5.6

+20 734434.5 94.2 5.8

-10 737746.0 94.6 5.4

-20 738555.3 94.7 5.3


28 NOVEMBER 2014


10%

15%

20%


WEST EAST

SOUTH

NORTH

0 - 5

5 - 10

10 - 15

15 - 20

20 - 25

25 - 30

30 - 35

10%

15%

20%


WEST EAST

SOUTH

NORTH

0 - 5

5 - 10

10 - 15

15 - 20

20 - 25

25 - 30

30 - 35

Wind Rose – DK07

uncertainty of virtual met mast measurements in dk · pdf fileuncertainty of virtual met mast...

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