Triple Phase Shift Modulation for Active Bridge Converter: Deep Reinforcement Learning-Based Efficiency Optimization

A triple phase shift (TPS) modulation strategy is proposed for a three-port active bridge (TAB) converter in shipboard zonal DC systems. Unlike traditional multi-port converters, the TAB realizes voltage conversion and bidirectional power conversion under TPS modulation. It exhibits superior performance in reducing control complexity, enhancing fault-tolerant capability, and extending the zero-voltage switching (ZVS) region under normal and fault operation modes. To further enhance its conversion efficiency, a deep reinforcement learning optimization approach based on the deep deterministic policy gradient (DDPG) algorithm is introduced to adaptively optimize TPS control parameters and minimize the overall power losses of the converter. To verify the proposed TPS modulation and DDPG-based optimization strategy for the TAB converter topology, a corresponding hardware prototype is built and experimentally tested under different operating conditions. Experimental results demonstrate that the TAB architecture with DDPG optimization effectively reduces current stress and power loss, boosting the converter’s maximum efficiency to 96.9% under normal mode and a 3% efficiency gain after fault isolation.