DQN Based Grid-Connected Single-Phase Inverter

This paper investigates the controller for grid-connected 1-Φ inverter utilizing H-bridge configuration, focusing on the application of reinforcement learning through the Deep Q-Learning Network (DQN) algorithm. H-bridge inverters are essential for converting DC into AC for seamless integration into the grid. The proposed approach employs DQN to optimize control strategies, allowing the inverter to dynamically adapt to varying grid conditions and load requirements. The study emphasizes the importance of maintaining power quality by synchronizing output current with grid voltage while minimizing harmonic distortion. Advanced control techniques, including digital Pulse-Width Modulation (PWM), are implemented to enhance inverter performance. A comprehensive simulation environment in MATLAB is developed to train the DQN agent, enabling it to learn optimal control policies through real-time interaction with the inverter system. The simulation results demonstrate significant improvements in response time, stability, and efficiency compared to traditional control methods viz Proportional Integral (PI) control. This research highlights the potential of integrating reinforcement learning techniques with H-bridge inverter technology. Overall, the findings contribute to advancing smart grid solutions by providing robust and adaptive control mechanisms that promote sustainable energy integration and enhance grid reliability.