Deep reinforcement learning-aided minimum current control for the DBSRC based on harmonic analysis method

Abstract

Aiming to optimize the modulation efficiency for the dual-bridge series-resonant converter (DBSRC), this paper proposes an deep reinforcement learning (DRL) aided EPS (DEPS) modulation scheme for minimum current operation based on the harmonic analysis method. Using the deep deterministic policy gradient (DDPG) algorithm as an advanced DRL algorithm, the scheme obtains the optimized modulation scheme DEPS through offline training of the agent, which can adopt the extended-phase-shift (EPS) modulation scheme and consider the zero-voltage-switching (ZVS) constraints. Thus, the trained agent of the DDPG which likes an implicit function, can provide optimal phase shift angle for the DBSRC in real-time with the minimum current and soft switching performance in the continuous operation range. Compared with the existing EPS modulation schemes using First Harmonic Approximation (FHA), the DEPS modulation scheme has similar operational efficiency and performance of the converter, while also possessing the ability to obtain modulation angles in real-time based on environmental parameters. Finally, PSIM simulation verifies the effectiveness of the proposed optimization scheme.