A Review on the Applications of Reinforcement Learning Control for Power Electronic Converters

Modern microgrid systems are facing challenges of small inertia, low damping, and unpredictable environments. Meanwhile, traditional control strategies for power electronic converters are characterized by high model dependency, slow dynamic response, and static control parameters, which make them inappropriate for the development of modern microgrid systems. In response to this problem, researchers have proposed reinforcement learning (RL) control strategies to achieve fast adaptive control through real-time interaction with the environment. A research review of existing RL control strategies for power electronic converters and provides prospects for their future practical application.