基于DDPG算法的风力发电机变桨距控制研究

风电机组模型的不确定性以及风速等外部干扰严重影响风电机组输出功率的稳定性，基于准确风机参数的传统控制策略难以满足系统控制需求。因此，本文提出一种基于DDPG算法的风机变桨距控制器。借助强化学习仅需与环境交互无需建模的优势，以风机模型为训练环境，功率为奖励目标，变桨角度为输出，采用深度神经网络搭建Actor-Critic单元，训练最优变桨策略。采用阶跃、低湍流、高湍流三种典型风况对算法进行检测。仿真结果表明，不同风况下基于DDPG算法控制器的控制精度、超调量、调节时间等性能均优于传统比例-积分-微分控制器效果。

Variable pitch control of wind turbine based on DDPG algorithm

The stability of wind turbine output power is seriously affected by the uncertainty of wind turbine model and external disturbances such as wind speed. The control strategy based on accurate fan parameters is difficult to meet the control requirements of the system. Therefore, a fan pitch controller based on the DDPG algorithm was proposed in this study. With the advantage that reinforcement learning only needs to interact with the environment without modeling, the fan model was taken as the training environment, the power was taken as the reward target, and the paddle angle was taken as the output. The deep neural network was used to build the actor-critic unit to train the optimal paddle variable strategy. Three typical wind conditions, namely step, low turbulence and high turbulence, were used to test the algorithm.The simulation results showed that the controller based on the DDPG algorithm had better control accuracy, overshoot and regulating time than the traditional proportional-integral-differential controller under different wind conditions.