AI-Driven Optimization in Power Electronics: Enhancing Efficiency and Performance

Power electronics systems demand more energy efficiency, better power loss margins, and improved stability. Traditional control techniques such as Fixed Switching Frequency Control (FSFC) and Fuzzy Logic Control (FLC) are limited due to their lack of fast response, adaptability, and performance in dynamic load conditions. This research presents an AI-based optimization framework by implementing machine learning-based control with AI-driven optimization for predictive decision-making. The optimization framework can enhance energy efficiency by 6.3 percent relative to FSFC and 2.5 percent relative to FLC, decrease power losses by 27.4 percent, and improve thermal stability by 14.5 percent. Furthermore, the response time improved by 38.2 percent, allowing for faster adaptation to load variations. The AI-based optimization approach effectively minimizes overall switching losses and system reliability. The experimental tests demonstrate that AI-based optimization can achieve significantly better outcomes than traditional approaches, demonstrating a scalable and adaptive system in support of smart power electronics and energy management and establishing a foundation for intelligent and more efficient power-control system development.