Applications of Machine Learning in Power Electronics: A Specialization on Convolutional Neural Networks

Abstract

Recently, there has been a lot of interest in integrating machine learning methods, specifically Convolutional Neural Networks (CNNs), with power electronics. An overview of the many developments and applications at the nexus of machine learning and power electronics is given in this review paper. We investigate how CNNs might help power electronics systems overcome obstacles and become more reliable and efficient. The study reviews the state of the field now and identifies areas for potential future research. According to the study, machine learning—especially Convolutional Neural Networks (CNNs)—is a promising field in electrical engineering with substantial promise for defect detection, classification, and pattern recognition when integrated into power electronics. Future directions emphasize integrating data sources, expanding real-time implementation, and optimizing the integration of renewable energy sources for higher efficiency and sustainability in power electronics. Challenges include the requirement for high-quality data and real-time implementation.