Artificial Intelligence-Aided Minimum Reactive Power Control for the DAB Converter Based on Harmonic Analysis Method

With the aim of reducing the reactive power for the dual-active-bridge (DAB) converter, this letter proposes an artificial intelligence (AI) aided minimum reactive power control scheme based on the harmonic analysis method. Specifically, as an advanced algorithm of the deep reinforcement learning (DRL), the deep deterministic policy gradient (DDPG) is used to train an agent off-line. During the training of DDPG algorithm, the three-phase-shift (TPS) modulation is adopted and the zero-voltage-switching (ZVS) constraints are considered. Thus, the trained agent of the DDPG which likes an implicit function, can provide optimal control strategies for the DAB converter in real-time with the minimum reactive power and soft switching performance in the continuous operation range. Finally, experimental results validate the feasibility and correctness of the proposed AI based optimized method.