Application of machine learning in power electronic system parameter optimization and adaptive control

This research focuses on implementing machine learning in optimizing parameters and controlling power electronic systems and suggests a fusion optimization technique using neural network and genetic algorithm. Initially, a mathematical model is developed for optimizing parameters in a power electronic system, with the utilization of genetic algorithm for optimization. Next, a system for adaptive control is created, incorporating the use of neural network for both parameter estimation and optimization of control strategy. By analyzing a specific case of an electric vehicle charging system, it was found that this approach greatly enhances the system's charging efficiency and stability. The results from the experiment confirm that this method is effective and practical for optimizing power electronic systems, offering fresh ideas and techniques for system design and control improvement.