Multi-Agent Soft Actor-Critic Aided Active Disturbance Rejection Control of DC Solid-State Transformer

The dc solid-state transformer (dcSST) plays a vital role in interconnecting diverse dc sources and loads in dc microgrids. However, output voltage regulation and submodule power balance control have always been two essential control challenges of the dcSST. To address these challenges, this article proposes a multiagent soft actor-critic-based active disturbance rejection control (MASAC-ADRC) method. The ADRC method is used for uncertainty estimation and compensation in modular dcSST. Specifically, the controller gains of the ADRC method are optimized adaptively by the MASAC method, thus enhancing the adaptability to changing conditions from the environment. Compared with the existing controller gain optimization methods, the proposed method does not rely on predetermined datasets. Instead, it provides a tailored strategy, employing a neural network to map optimal ADRC parameters from the measured states. Through the incorporation of diverse environmental scenarios, encompassing variant input voltage and output power, the MASAC-ADRC method achieves superior dynamic performance. Experimental validation underscores the efficacy of the proposed algorithm, showcasing its superiority over alternative approaches. The proposed method yields enhancements exceeding 50% in dynamic performance metrics such as overshoot, settling time, and mean square error when compared with existing methods.