Dr. Cathérine Mei β ner a Dr. Arne R. Gravdahl a Dr. Xuan Wu b
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Transcript of Dr. Cathérine Mei β ner a Dr. Arne R. Gravdahl a Dr. Xuan Wu b
Short-term Forecasting using Mesoscale Simulations, Neural Networks and CFD Simulations
EWEA 2012 Annual Event 16-19 April 2012Copenhagen
Dr. Cathérine Meiβnera Dr. Arne R. Gravdahla Dr. Xuan Wub
a WindSim AS, Fjordgaten 15, N-3125 Tønsberg, Norwayb WindSim AMERICAS, 470 Atlantic Avenue, Boston, MA 02210, US
EWEA 2012 Annual Event,Copenhagen
• The forecasting procedure
• Validation on a Chinese wind farm
• Conclusion & Outlook
Content
EWEA 2012 Annual Event,Copenhagen
Understanding how much your wind farm will produce in the next hours is crucial to make the right decisions, either in the energy market or for maintenance planning.
WindSim has developed a system coupling • Mesoscale numerical weather forecasts (Mesoscale)• Artificial Neural Networks (ANN) and • Computational Fluid Dynamics (CFD) to give the best predictions on your site.
This system is under development together with partners from the energy trading business and will be validated on different sites all over Europe in the next months.
The Procedure: Coupling Mesoscale, ANN and CFD
EWEA 2012 Annual Event,Copenhagen
Mesoscale hindcasts
Historical wind
measurements
The Procedure: Coupling Mesoscale, ANN and CFD
WindSim Power Production Forecast
GLOBAL FORECAST
MESOSCALE FORECAST
Neural Network correction
WindSim wake model
WindSim CFD Downscaling
Neural Network Training
Set-up period Forecasting mode
CFD look-up tables
EWEA 2012 Annual Event,Copenhagen
The Procedure: Coupling Mesoscale and CFD
Global Models100 - 16 km
e.g. ECMWF, GFS
Regional Models9 - 1 km
e.g. WRF
Micro Model100 - 10 mWindSim
Description of the atmosphericconditions
Accurate descriptionof the local flow fieldand the wake effects
EWEA 2012 Annual Event,Copenhagen
Transfer of mesoscale data into the CFD by using a virtual met mast solution: The forecast of one point in the mesoscale model inside the CFD domain is selected and used to scale the CFD model results at every turbine position
Advantage: Very fast as CFD look-up tables can be produced in the set-up phase of the forecasting system and no CFD simulation is necessary during the actual forecast.
Regional Model Micro ModelWindSim
The Procedure: Coupling Mesoscale and CFD
EWEA 2012 Annual Event,Copenhagen
The Procedure: Why use Artificial Neural Networks?
WRFWRF_ANNMEAS
WRF data has phase and model bias errors in wind speed and direction
Trained networks can be used to correct each forecasted time series from the mesoscale model before it is used in the CFD simulation
January February w
ind
spee
d (m
/s)
0
5
10
15
2
0
EWEA 2012 Annual Event,Copenhagen
The Procedure: The added value of using CFD
500m 20 m
The CFD describes more accurately the local flow field around the turbines and can therefore downscale the mesoscale model results
The CFD is able to calculate the wake corrected energy production.
EWEA 2012 Annual Event,Copenhagen
Validation – Chinese wind farm
New legislation in China requires an operational forecasting system for all wind farms
Validation site in China:
Site with 6 measurement masts and 11 turbines Complexity of the site: steepness up to 45 degrees around the
turbine area and absolute height differences of 1000 m WRF simulations run for 4 months in the winter season on 1 km
resolution WRF results for wind speed and wind direction extracted for every
met mast position in the area
EWEA 2012 Annual Event,Copenhagen
Validation – Chinese wind farm
Wind Frequency Rose0°
22.5°
45°
67.5°
90°
112.5°
135°
157.5°
180°
202.5°
225°
247.5°
270°
292.5°
315°
337.5°
0%
10%
20%
30%
40%
WRFMEAS
1. The WRF mesoscale model predicts the monthly mean wind speed and direction well but the absolute value is too high
WRFMEAS
Mast 2
EWEA 2012 Annual Event,Copenhagen
Validation – Chinese wind farm
2. The Neural Networks are able to correct this deviation in wind speed and wind direction
Forecast
Speed RMSE(m/s)
NN 1 Speed RMSE(m/s)
NN 2 Speed RMSE(m/s)
Forecast Direction
RMSE (deg)
NN 1 Direction
RMSE (deg)
NN 2 Direction
RMSE (deg)
Mast 2 3.7 1.7 1.8 34 15 26
Mast 4 3.7 1.6 2.1 53 16 41
Mast 5 3 1.7 1.3 63 21 43
Mast 6 3.3 2 2.3 61 17 42
Average 3.43 1.75 1.88 52.75 17.25 38
EWEA 2012 Annual Event,Copenhagen
Validation – Chinese wind farm
3. The CFD modelling improves the modelling of the wind profile
Mast 3Mast 2
EWEA 2012 Annual Event,Copenhagen
Validation – Chinese wind farm
4. The CFD is used to calculate the wind speed at the turbines
Turbine 2
EWEA 2012 Annual Event,Copenhagen
Validation – Chinese wind farm
5. The CFD is used to calculate the energy output at the turbines – including wake effects
Turbine 2
EWEA 2012 Annual Event,Copenhagen
A forecasting system has been set-up coupling Mesoscale, ANN and CFD
The system has been validated on a Chinese wind farm in complex terrain
Giving an ANN corrected wind speed delivers improved energy calculation when using CFD compared to using mesoscale data directly
Validation is on going with production data from wind farms in Europe
Searching for partners/users who want to validate the system
Conclusions & Outlook