Wind Energy

29 Online Modeling of Wind Farm Power for Performance Surveillance 165
Metmast
Fig. 29.1. Bassens wind farm layout showing position of metmast and three turbines
selected for wind farm model assessment. Free metmast sector between 203 ◦ and 15 ◦
(in meteorological convention: 0 ◦ N, clockwise positive)
29.3 Results
Comparison of measured and modeled power (Pmeas and Pmod) is performed
by means of mean error and root mean squared error calculated as ME =
Pmeas,i − Pmod,i and RMSE =
� 1
N
21
� (P ′ meas,i − P ′ mod,i )2 , respectively.
Data were selected for wind directions for which the metmast was receiving
free inflow (see Fig. 29.1); enabling study of three different characteristic turbines.
Turbine 1 experiences the lowest wake effect, while turbine 21 observes
wakes from every direction and turbine 25 for some cases is unaffected and
whitstands the greatest amount of wakes for wind from SW.
In average the wind farm model shows very good agreement with the
averaged measured data. As expected, turbines affected by wake during a
greater amount of time show higher mean error (see Figs. 29.2 and 29.3).
Moreover it can be observed that Ainslie model performs better than Jensen
model. However, a tendency to underpredict power for low and overpredict
for high wind speeds shows a need in calibration of the thrust coefficient.
The uncertainty for both models is high, especially for low wind speeds,
and almost no difference is observed between them. It is to remark that the
RMSE presents a dependency with respect to the inflow wind speed and that
great part of this uncertainty is related to fluctuations of wind speed and
power at each turbine, i.e., to power curve estimation.