Research on Blade Pitch Control Optimization of Wind Turbines under Extreme Wind Speed Conditions Based on Deep Reinforcement Learning

In order to address the issues of response hysteresis and insufficient wind resistance in the blade pitch control systems of wind turbines under extreme wind speed conditions, this paper proposes an intelligent pitch control optimization strategy based on Deep Reinforcement Learning (DRL). By constructing a wind farm environment simulation platform and introducing the Proximal Policy Optimization (PPO) algorithm to train the intelligent controller, adaptive real-time adjustment of the turbine blade pitch angle is achieved. This method introduces a perception-decision-execution integrated mechanism on the basis of traditional control strategies, fully exploring the high-dimensional dynamic relationship between wind speed disturbances and the response of the wind power system. Simulation results show that the proposed strategy reduces response time by about 32%, decreases average power output fluctuation by about 44%, and effectively reduces the fatigue loads on the turbine, improving the stability and structural safety of the wind power system. The results demonstrate that the application of the DRL algorithm in wind turbine pitch control has significant advantages and provides strong support for the safe operation of turbines in complex environments.