Reward adaptive wind power tracking control based on deep deterministic policy gradient

Abstract

Wind power efficiency is an essential factor affecting wind power development, and efficient wind power control methods are the key to improving wind power efficiency. Previous wind power control methods require high internal system expertise in internal systems and struggled to balance the accuracy of the maximum power control and the output stability under high wind speed. Therefore, this paper proposes a reward-adaptive control method for wind power tracking based on Deep Deterministic Policy Gradient (DDPG). The method can use one controller to simultaneously control the generator torque and pitch angle in various operating conditions following the system state and the designed flag of operating conditions, thus enabling efficient tracking of the maximum power generation, and stabilizing the output power under high wind speed. Moreover, this paper proposes an internal and external reward algorithm that integrates the Intrinsic Curiosity Module (ICM) and the Actor–Critic (AC) architecture, which can adaptively calculate the reward of DDPG, thereby effectively resolving the sparse reward problem, accelerating the convergence of neural network, and improving the learning effect. From the simulation, the control method proposed in this paper can effectively improve the power generation efficiency under turbulent wind speed, reduce the pitch angle variation by about 30%, and improve the power tracking accuracy by more than 70%.