Autonomous Input Voltage Sharing Control and Triple Phase Shift Modulation Method for ISOP-DAB Converter in DC Microgrid: A Multiagent Deep Reinforcement Learning-Based Method

Yu Zeng*, Josep Pou, Changjiang Sun, Suvajit Mukherjee, Xu Xu, Amit Kumar Gupta, Jiaxin Dong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

This article proposes a multiagent (MA) deep reinforcement learning (DRL) based autonomous input voltage sharing (IVS) control and triple phase shift modulation method for input-series output-parallel (ISOP) dual active bridge (DAB) converters to solve the three challenges: the uncertainties of the dc microgrid, the power balance problem, and the current stress minimization of the converter. Specifically, the control and modulation problem of the ISOP-DAB converter is formed as a Markov game with several DRL agents. Subsequently, the MA twin-delayed deep deterministic policy gradient (MA-TD3) algorithm is applied to train the DRL agents in an offline manner. After the training process, the multiple agents can provide online control decisions for the ISOP-DAB converter to balance the IVS, and minimize the current stress among different submodules. Without accurate model information, the proposed method can adaptively obtain the optimal modulation variable combinations in a stochastic and uncertain environment. Simulation and experimental results verify the effectiveness of the proposed MA-TD3-based algorithm.

Original languageEnglish
Pages (from-to)2985-3000
Number of pages16
JournalIEEE Transactions on Power Electronics
Volume38
Issue number3
DOIs
Publication statusPublished - 1 Mar 2023

Keywords

  • Input-series output-parallel-connected dual active bridge (ISOP-DAB) converter
  • input voltage sharing (IVS)
  • multiagent twin-delayed deep deterministic policy gradient (MA-TD3)
  • triple phase shift modulation

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