Review of Machine Learning Techniques for Power Electronics Control and Optimization

TL;DR

This paper provides a comprehensive review of how machine learning techniques are applied to enhance control and optimization in power electronics, highlighting their potential to improve efficiency and functionality across various applications.

Contribution

It offers an extensive overview of machine learning methods used in power electronics, emphasizing their roles in advancing control and optimization strategies.

Findings

Machine learning techniques significantly improve power electronics control.

ML methods enable more efficient and adaptive power systems.

The review identifies promising future research directions.

Abstract

In the rapidly advancing landscape of contemporary technology, power electronics assume a pivotal role across diverse applications, ranging from renewable energy systems to electric vehicles and consumer electronics. The efficacy and precision of these power electronics systems stand as cornerstones of their functionality. Within this context, the integration of machine learning techniques assumes paramount significance. This article endeavors to present an extensive and comprehensive review of the machine learning techniques that find application in power electronics control and optimization. Through meticulous exploration, we aim to elucidate the profound potential of these methods in shaping the future of power electronics control and optimization.