Deep Reinforcement Learning-Based Control Scheme for Performance Enhancement of PMSG Wind Turbine with Vienna Rectifier

A novel control scheme based on deep reinforcement learning (DRL) is presented to improve the operational performance of permanent magnet synchronous generator (PMSG) with Vienna rectifier (PGVR) in wind turbine generator system. This paper investigates the harmonics problem of stator current in PMSG and the fluctuation problem of neutral point voltage in Vienna rectifier. First, a wind speed-based reward function with variable weight coefficients is designed to realize the intelligent operation of PGVR control system. Second, a fast response Agent model with wind speed as the first observation state is established to minimize the influence of external environment on PGVR control system. Finally, diverse stochastic training environments are elaborated to ensure that PGVR control system has enough experience to cope with different wind speed variation scenarios. Twin delay deep deterministic policy gradient (TD3) algorithm is used for offline training. Simulation and experimental results show that the proposed scheme has small control errors at different wind speeds and effectively improves power quality and generation efficiency.