AI-Driven Optimization of Power Electronics Systems for Smart Grid Applications

Abstract

The high growth of smart grid infrastructure requires efficient, reliable, and intelligent power electronics system that can adapt to dynamic load conditions and integrate the renewable source of energy. This paper creates a complete holistic framework for optimizing power electronics systems using AI methods, such as machine learning (ML) and reinforcement learning (RL), for better performance, efficiency and grid resilience. It is proposed a hybrid AI model to optimize converter topologies, switching strategies and power flow control in real time. Using MATLAB/Simulink simulation results show improved system efficiency, lower total harmonic distortion (THD) and increased voltage regulation when compared with the conventional control. The findings highlight the promise of power electronics revolutionizing in future smart grid applications offered by AI.