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

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.

Automated summary

This article proposes a multiagent deep reinforcement learning (DRL) based approach for controlling input voltage sharing and modulation in an input-series output-parallel dual active bridge converter. The proposed method addresses the challenges of uncertainties in the dc microgrid, power balance, and current stress minimization. The control and modulation problem is formulated as a Markov game and solved using the MA twin-delayed deep deterministic policy gradient (MA-TD3) algorithm. Simulation and experimental results demonstrate the effectiveness of the proposed approach.