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

Peixin Zheng; Diansheng Yang

doi:10.1117/12.3054456

11 December 2024 Application of machine learning in power electronic system parameter optimization and adaptive control

Peixin Zheng, Diansheng Yang

Proceedings Volume 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024); 134451U (2024) https://doi.org/10.1117/12.3054456
Event: International Conference on Electronics. Electrical and Information Engineering (ICEEIE 2024), 2024, Haikou, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Peixin Zheng and Diansheng Yang "Application of machine learning in power electronic system parameter optimization and adaptive control", Proc. SPIE 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024), 134451U (11 December 2024); https://doi.org/10.1117/12.3054456

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