Multivariable integrated power control optimization of wind farms based on deep reinforcement learning
China has proposed the"dual carbon"strategy,aiming to build a new power system with renewable energy as the primary component.Based on real wind farm data,optimization control strategies were proposed to improve the wind farm's output power,thereby further enhancing wind energy utilization.The wake effects between wind turbines were the main focus,and a wind farm power multivariable optimization control strategy based on model-free deep reinforcement learning(DRL)was proposed.The strategy employed the Proximal Policy Optimization(PPO)algorithm to optimize multiple variables,including the yaw angle,tilt angle,blade pitch angle,and tip speed ratio(TSR)in a dynamic wind farm.Through intelligent agents learning from the data generated by the agent during operation,an optimal control strategy was obtained,overcoming the limitations of traditional mathematical optimization methods.Simulation results showed that,compared to existing wind turbine control algorithms,the model-free DRL-based multivariable optimization control strategy significantly improved computational efficiency,reduced the difficulty of parameter optimization,and optimized the direction and strength of the wake.The optimized average output power was increased by 37.08%.
wind farmdeep reinforcement learningwake controlmultivariable controlpower optimizationPPO"dual carbon"strategynew power system
国家科技期刊平台
NSTL
万方数据
维普
