A Review on the Applications of Reinforcement Learning Control for Power Electronic Converters
Peng Chen, Jianfeng Zhao, Kangli Liu, Jingyang Zhou, Kun Dong, Yufan Li, Xirui Guo, Xin Pan
Abstract
In modern micro-grid systems, the control of power electronic converters faces numerous challenges, including the uncertainty of parameters of the controlled objects, variations in the operating environment, and the increased complexity of models for controlled objects. 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 micro-grid 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. However, there is currently a lack of comprehensive literature reviews specifically focusing on RL control for power electronic converters. To fill this gap, this article provides a summary of existing published research papers. Specifically, the mainstream RL algorithms are listed. The existing RL control strategies for various power electronic converter topologies are introduced. The sim-to-real methods for practical implementation are illustrated. The underlying design considerations are discussed. Moreover, the future research prospects are explored.