Deep Q-Learning Control for Voltage Source Inverters: A Practical Guideline

Abstract

Deep reinforcement learning (DRL) has demonstrated promising results in power electronics control, particularly for current regulation of voltage source inverters (VSIs). However, its practical adoption remains limited due to the perceived complexity of neural network design, training, and real-time implementation. This work presents a practical, step-by-step, engineering-oriented guideline for implementing Deep Q-Learning (DQN) control in three-phase VSI applications. The proposed methodology encompasses the definition of state and action spaces, reward function design, neural network architecture selection, simulation-based training, and deployment on embedded real-time hardware. The guideline is specifically tailored for power electronics engineers without prior expertise in artificial intelligence. Experimental results obtained on a real-time test bench demonstrate model-free operation, accurate current tracking and real-time feasibility, supporting the applicability of the proposed approach for practical converter control.