Abstract
The wake effect of wind farms is one of the main factors causing a reduction in the power output. Cooperative yaw control has the potential to mitigate wake effects and increase the overall output power of the wind farm. Optimizing the yaw control strategy is challenging, due to the large number of degrees-of-freedom and interaction of wake flow in the farm. Here a genetic algorithm is applied, using the Baskankhah and Porté -Agel yawed wake model, and seeking to maximize the overall output power of the wind farm as the optimization objective. Performance is demonstrated on a reference wind farm containing 49 wind turbines of two different types. The increase in wind farm power due to cooperative yaw optimization is 1%-2%. When the wake effect is particularly strong, the maximum is up to 10%. The optimal yaw angle of the upstream wind turbine is larger than that of the middle wind turbine, while the downstream wind turbine may not yaw.