Reinforcement Learning Based Modulation for Balancing Capacitor Voltage and Thermal Stress to Enhance Current Capability of MMCs

Abstract

Balancing DC capacitor voltage of many submodules (SMs) is one of the important issues in modular multilevel converter (MMC) systems. In addition, the balance of thermal stress between SMs should be considered to equalize the lifetime expectation of semiconductors and to enhance the current capability of MMC systems. However, it is complicated to balance all the various factors satisfactorily at the same time. Recent machine learning (ML) techniques can achieve optimal results through learning using numerous data acquired in complex environments. Therefore, this paper proposes a new modulation based on reinforcement learning (RL), which is a subclass of ML methods, to optimally balance the capacitor voltage and thermal stress of SMs. A deep Q-network (DQN) agent, which is one of the RL algorithms, is applied in accordance with a nearest-level modulation (NLM), and main features of the DQN agent are described in this paper. The effectiveness of the proposed modulation based on RL is verified by simulations results.