A Deep Reinforcement Learning Framework for 3L-NPC-DAB Converters With Multiple-Degree-of-Freedom Phase Shift Control

The three-level neutral-point-clamped dual-active-bridge (3L-NPC-DAB) converter offers several advantages over the conventional two-level DAB converter, including enhanced control flexibility and the ability to operate with lower voltage-rated power switches. However, with the increase in the number of power devices and multiobjective control requirements, traditional phase shift control cannot meet the requirements. In order to further improve the conversion efficiency, this study introduces a deep reinforcement learning (DRL) optimization scheme based on the five control degrees of freedom (5-DoF) technique. The proposed scheme utilizes the deep deterministic policy gradient (DDPG) algorithm to minimize power losses and determine optimal control solutions. Through training, the DDPG agent acts as a predictor, enabling effective control decisions for maximizing conversion efficiency across various operating conditions. The effectiveness of the proposed method is validated through experimental results obtained from a laboratory prototype.