Work

An adaptive quasi-PR controller for modular multilevel converters based on deep reinforcement learning

Year: 2025

Type: article

Abstract: 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.

Source: Frontiers in Energy Research

DOI: 10.3389/fenrg.2025.1716873

Authors: Yukun Chen, Xin Luo, Yuxin Lu, Fei Duan, Zhu Guo, Tianyou Yan

Institutions: China Southern Power Grid (China), Guangdong Power Grid Company (China)

Language: English

Topic: HVDC Systems and Fault Protection

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,077