An efficient optimization algorithm for active yaw control to increase wind farm power while considering load reduction

Active yaw control can improve the power output of wind farms. However, the existing optimization algorithms generally require long computational times. Wind turbine loads are seldom considered. This study proposes a computationally efficient optimization algorithm for active yaw control of large wind farms. This method can simultaneously maximize the wind farm power and minimize the wind turbine load. It first determines the wind turbine pairs that have wake interactions. The large wind farm is then divided into several line-arranged farms. The line-arranged farms are further divided into groups that have three wind turbines. By looping the groups from downstream to upstream, the yaw angles can be optimized. The proposed method is compared with the differential evolution algorithm and the sequential least squares programming method. In terms of power improvement, these three methods perform almost the same, with a maximum difference of only 4 %. In terms of load reduction, the proposed method performs the best and is 51 % better than the programming method. The computational time of the proposed method is 517 times and 199 times faster than those of the differential evolution algorithm and programming method, respectively. The proposed method is effective at improving the power and reducing the load.