commercial wake models on data from offshore wind farms and highlight the issues ... In our benchmarking trial we compar
Benchmarking of wake models for offshore wind farms
PO.ID B005
Nicolai Gayle Nygaard, Rasmus Sandbech Clausen, Martin de Maré, and Raymond Downey
DONG Energy Wind Power Abstract
Methods
• Results are based on analysis of 10-minute SCADA data • Wind speed derived from power production and power curve
• Gross power from free wind speed and power curve averaged over validation sample • Net power is average of produced power over validation sample
Park Power curve for entire windfarm 1 0.8 0.6 0.4
• Data filtered for turbine availability
0 5
1
6284
0.99
6282 0.98
Northing [km]
6280
10
15 20 Free wind speed [m/s]
0.96
6274 0.95 6272 6270
0.94
6268
0.93
635
640
645
Easting [km]
Wind farms In the selection of wind farms for the is analysis the primary criteria has been that an adequate period of data should be available where there are no significant changes in wake and operation over time. The following wind farms have been analysed: Generator Rotor CommisNumber of Name size diameter sioning turbines [MW] [m] year Horns Rev 1 2.0 80 80 2002 Horns Rev 2 2.3 93 91 2009 Burbo Banks 3.6 107 25 2008 Barrow 3.0 90 30 2006 Anholt 3.6 120 111 2013 Nysted 2.3 82 72 2003 Gunfleet Sands 3.6 107 48 2010
Only periods with no change in wake impact from neighbouring wind farms are considered.
Ho r ns
R ev
Ho r ns 1
R ev
2
Bur bo B
an k s
Bar r ow
Anh olt
Ny s ted
Gun flee tS
Ny s ted
Gun flee tS
and s
20
0
-20
-40
Modelled wake loss: • Models run with 1 m/s resolution in wind speed and 1 degree resolution in wind direction • Gross power from power curve and validation sample wind speed frequency table • Net power from model runs and validation sample wind speed and wind direction frequency table
Model setups: • PARK: wake decay parameter k=0.04 • Eddy viscosity: turbulence intensity of 6%
PARK k=0.04 Eddy Viscosity LWC Eddy Viscosity no LWC Ho r ns
R ev
Ho r ns 1
R ev
2
Bur bo B
an k s
Bar r ow
Anh olt
and s
In the figure below the absolute model errors are shown as function of farm size in number of turbines. Sector size 5 degrees 35 30 25
PARK k=0.04 Eddy Viscosity LWC Eddy Viscosity no LWC
20 15 10 5 0 20
Sector results It is illustrative to compare predicted and observed wake loss in wind direction sectors. Same procedure as above, but applied in each sector. In the figure below the sector-wise efficiency for Anholt is shown with a sector size of 5 degrees.
Windfarm : Anholt, Sector size 5 1
40
60 80 Number of turbines
100
120
Conclusions For the PARK model and the Eddy viscosity model with the large wind farm correction the results generally shows errors between 5% and 15%. However for the eddy viscosity with LWC the results are in all cases AEP conservative while the PARK model goes in both directions. Without the large wind farm correction the Eddy viscosity model gives significantly larger errors. The sector size used has an influence on the results but the general trends are the same.
0.95
Sector efficiency [-]
630
PARK k=0.04 Eddy Viscosity LWC Eddy Viscosity no LWC
25
6266 0.92
-20
Sector size 30 degrees
• Modelled wake loss: Predicted = 1 – Netmodel/Grossmodel
• Free wind speed is mean of wind speed of unwaked turbines
6286
On the figures below the results are shown for 5 and 30 degreed sector sizes: Sector size 5 degrees 20 10
In the figure below an example of a park power curve based on SCADA data for 12 sectors is shown.
0.2
ANH total production relative to best WTG
• Negative model error = model AEP optimistic
• Observed wake loss: Observed = 1 - Netobs/Grossobs
• Wind direction from turbine yaw signals (calibrated)
The figure illustrates the wake losses for individual turbines in the Anholt wind farm.
• Positive model error = model AEP conservative
abs(Model error)
Using SCADA data
• Validation sample is all samples with at least 95% of all turbines available
Model error = 100% x (Predicted – Observed) / Observed
Model error [%]
We contrast the performance of the WindFarmer eddy viscosity model with that of the N.O. Jensen based PARK model as implemented in WindPRO/Wasp and additionally evaluate whether the eddy viscosity model requires a large wind farm correction (LWC) based on added roughness to account for wake losses in modern offshore wind power plants.
Observed wake loss:
The error in the wake model has been defined as the relative difference in the predicted and observed wake losses:
Model error [%]
A key ingredient is the validation of the wake loss prediction, since the wakes represent the principal component of the wind power plant losses. In this study we perform a validation of commercial wake models on data from offshore wind farms and highlight the issues and choices involved when analysing SCADA data for benchmarking studies. In our benchmarking trial we compare the commercial software package WindFarmer to operational data from several offshore wind farms.
To assess the performance of the wake models the efficiency predicted by the models is compared to the observed efficiency of the wind farms based on the SCADA data.
Power normalized [-]
The decision to develop and construct a wind power plant is based to a large extent on the capital expenditure and the expected energy yield of the wind farm over time. It is of paramount importance for wind power plant developers to thoroughly analyse the post-construction yield of the operating assets in order to challenge the assumptions and improve the modelling. In this way, the bias and uncertainty of future energy yield predictions may be reduced.
Model error
The last figure shows an increase in the absolute model error for the eddy viscosity model with the number of turbines. Using the large wind farm correction reduces the error, but the PARK model still gives the best results.
0.9
0.85
0.8
0.75 0
References
SCADA Eddy Viscosity LWC Eddy Viscosity no LWC PARK k=0.04 100
200 Sector [deg]
300
1. Jensen N O 1983 A Note on Wind Generator Interaction, Risø-M-2411 2. Kátic I, Højstrup J and Jensen N O 1986 A simple model for cluster efficiency EWEA 1986 3. GL Garrad Hassan 2013 WindFarmer V5.2 Theory Manual 4. Nygaard N G 2014 Wakes in very large wind farms and the effect of neighbouring wind farms, J. Phys.: Conf. Ser. 524 012162
Analysis of Operating Wind Farms 2014 - EWEA Technology Workshop, Malmö, 9-10 December 2014
