Fatigue analysis of wind turbine and load reduction through wind-farm-level yaw control

Wind-farm-level yaw-control-based power optimization can improve the total power output. However, previous related studies have rarely considered wind turbine fatigue loads during power optimization. Consequently, this study proposes a novel farm-level yaw control optimization algorithm that can maximize the total power output and simultaneously minimize the wind turbine fatigue loads. To implement this, the structural dynamics of a wind turbine under different wind speeds, turbulence intensities, and yaw angles are calculated first. The out-of-plane fatigue loads at the blade root are positively correlated with the yaw angle, and the out-of-plane fatigue loads at the yaw bearing and tower base are negatively correlated with the yaw angle. Subsequently, accurate meta models are trained to predict the wind turbine fatigue loads. They perform well, with the errors of the out-of-plane meta models all being smaller than 1.0 %. Finally, the yaw angles of the wind turbines are optimized via the differential evolution algorithm. The numerical results indicate that for the examined wind farms, the proposed optimization method can increase the total power output by at least 6.4 % and at most 7.8 %; moreover, it can reduce the added wind turbine fatigue loads caused by power optimization by at least 23.53 % and 52.25 % at most.