ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Smart Grids
This article is part of the Research TopicAdvanced Operation, Control, and Planning of Urban Power GridView all articles
An Adaptive Quasi-PR Controller for Modular Multilevel Converters Based on Deep Reinforcement Learning
Provisionally accepted
- 1Electric Power Research Institute, China Southern Power Grid, Guangzhou, China
- 2Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangzhou, China
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In modern power systems, Modular Multilevel Converter (MMC) plays an important role due to its advantages of convenient maintenance and easy expansion. However, because of its structural defects, it is easy to induce circulating current, which poses a challenge to stable operation. It is of great significance to develop efficient and reliable circulating current suppression technology. This paper introduces a Deep Reinforcement Learning (DRL) method for adaptive tuning of controller parameters, addressing the challenge of difficult parameter adjustment in the MMC circulating current suppression strategy employing a quasi-PR controller. It analyzes the feasibility of using the twin delayed deep deterministic (TD3) algorithm to tune the parameters of the PR controller, and designs a reasonable neural network and reward function to train the agent for control. Simulation results demonstrate the superiority of the TD3-based adaptive quasi-PR controller over the traditional fixed-parameter quasi-PR controller. The adaptive controller has a better effect on MMC circulating current suppression, with better dynamic response and smaller THD. This provides an effective solution for promoting the large-scale application of MMCs and enhancing the performance of power systems.
Keywords: modular multilevel converter (MMC), Quasi-PR controller, Circulating currentsuppression, Deep reinforcement learning (DRL), Optimized control
Received: 01 Oct 2025; Accepted: 27 Oct 2025.
Copyright: © 2025 Chen, Luo, Lu, Duan, Guo and Yan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yukun Chen, chenyk1@csg.cn
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