Improving Low-Cost Single-Phase Inverter Performance using DRL-Based Control System: Experimental Validation

Abstract

This paper presents the improvement of a low-cost, single-phase pure sine wave inverter controlled by a deep reinforcement learning (DRL) agent. The study addresses the challenge of lacking performance of low-cost inverter, which is primarily due to the stability requirements of conventional control strategies. A DRL- based control approach is proposed to enhance voltage and frequency stability while reducing the need for extensive manual tuning. The system is validated through both simulation and experimental verification in a microgrid islanded configuration. The results demonstrate that the DRL-based inverter effectively maintains 220 V_RMS at 50 Hz, achieving a stable root mean square voltage of 219.8 V, and a total harmonic distortion (THD) below 8%. The use of DRL making it an attractive solution for renewable energy systems, off-grid applications, and rural electrification. This study highlights the feasibility of DRL in power electronics and suggests that further optimization of training generalization and computational efficiency could enhance real-time and grid-tied deployment. The findings contribute to the advancement of intelligent inverter control, offering an alternative for next-generation microgrid and distributed energy systems.