Intelligent Control of an Active Front-End Converter: Deep Reinforcement Learning Approach

Deep reinforcement learning-based algorithms exhibit significant potential in developing robust model-free control systems for the next power converter generation. This work presents a control strategy based on a deep reinforcement learning (DRL) framework to operate an Active Front-End (AFE). The research's originality lies in finding an optimal control policy that leverages DRL's capabilities to enhance the AFE control performance, all without prior information regarding power converter dynamics and parameters. Moreover, the control strategy is designed to ensure the adaptability of the converter across diverse operational scenarios. To this end, multiple intelligent agents are developed, trained, tested, and validated using the AFE converter dynamics. Simulated results demonstrated that the proposed control methodology exhibits robustness, effectively handling uncertainties associated with the converter. Also, the empirical findings reveal that the proposed control strategy presents a solid performance in the current control and DC-link voltage control tasks, with a maximum Total Harmonic Distortion of 4.25% for 10 kHz sampling frequency.