Anti-tropical cyclone load reduction control of wind turbines based on deep neural network yaw algorithm

Rapid changes in the wind field of tropical cyclones can cause excessive loads and threaten the safety of offshore wind turbines. This paper designs a wind turbine yaw optimization strategy based on the deep neural network to reduce the structural loads of wind turbines caused by tropical cyclones. Firstly, the high-precision tropical cyclone data is used to analyze the characteristics of the wind field. Then, a pseudo-Monte Carlo experiment is designed to compensate for the incompleteness of the observed data. Furthermore, a robust nonlinear coupling model between the wind characteristics and the loads of the wind turbine is constructed by a deep neural network, and the optimal yaw angle is searched in real time based on this model. The simulation results show that the proposed deep neural network model based on pseudo-Monte Carlo scenario generation can robustly calculate the structural loads of wind turbines with a calculation error of less than 8 %. After applying this model to the real-time optimization control loop, the corresponding optimized yaw angle can be obtained according to the operating data of the wind turbine under tropical cyclone conditions so that the structural loads of the wind turbine are reduced. Compared with the no-yaw and traditional yaw strategies, the load suppression effect is 1 %–9 % under different working conditions. The proposed data-driven structural load model and load suppression algorithm will effectively improve the operating safety of wind turbines under tropical cyclone conditions.