Deep Reinforcement Learning-based Power Flow Control for Triple Active Bridge Converter

This paper presents a power flow control strategy based on deep reinforcement learning (DRL) for triple active bridge (TAB) converter. The DRL-based agent is designed to control TAB converter instead of conventional decoupling controller. A wide range of voltage and power operation conditions are used to train the agent offline, where deep deterministic policy gradient (DDPG) algorithm is employed to find appropriate shift phase angle with the constraint of minimizing transmission power errors. After training process, the well-trained agent is adopted to achieve decoupled power control online for TAB converter. Simulation and experiment results are presented to verify the effectiveness of the proposed DRL-based power control strategy. This approach enables multidirectional power flow control without decoupled matrix and look-up table, while it exhibits robust performance in response to input voltage variations.