This book provides a comprehensive guide for integrating machine learning techniques to enhance power electronic systems, with a focus on real-time applications, fault detection, and advanced control systems. Machine learning applications in power electronics delve into the transformative potential of machine learning in the field of power electronics. It is designed for professionals, researchers, and students who seek to leverage machine learning to address complex challenges and optimize performance in power electronics. The book explores the synergies between machine learning and power electronics, highlighting the importance of these technologies in industries such as renewable energy, electric vehicles, and industrial automation. It provides practical insights into implementing machine learning solutions, covering essential concepts, algorithms, workflows, and real-time deployment. Readers can gain valuable knowledge on integration strategies and advanced applications, including control of permanent magnet synchronous motor (PMSM) drives and fault detection in neutral point clamped (NPC) inverters. Additionally, the book offers best practices for selecting appropriate machine learning methods, such as integrating physics-informed models, utilizing lightweight neural networks, ensuring transparency with explainable methods, and employing conformal prediction for reliable outcomes. Beyond practical guidance, this book presents innovative ideas from recent literature, showcasing cutting-edge applications and future research directions. With its practical focus, detailed methodologies, and forward-looking insights, this book is an essential resource for anyone looking to harness the power of machine learning to drive innovation and improve system performance in power electronics.
Table of Contents:
Introduction to Machine Learning in Power Electronics.- Fundamentals of Machine Learning.- Integrating Machine Learning with Power Electronics.- Machine Learning Methods for Control of PMSM Drives.- Machine Learning for Fault Detection.- Recommendations and Best Practices.
About the Author :
Hasan Ali Gamal Al-kaf received the B.S. (Hons.) degree in electronic engineering and the M.S. degree in electrical engineering from Universiti Tun Hussein Onn Malaysia, Johor, Malaysia, in 2016 and 2018, respectively. He is currently pursuing the Ph.D. degree in electrical and computer engineering at Ajou University, Suwon, Korea. His main areas of research interests are power electronics, motor drives, multilevel inverters, artificial intelligence, and machine learning.
Prof. Kyo-Beum Lee received the B.S. and M.S. degrees in electrical and electronic engineering from Ajou University, Suwon, Korea, in 1997 and 1999, respectively. He received the Ph.D. degree in electrical engineering from Korea University, Seoul, Korea, in 2003. From 2003 to 2006, he was with the Institute of Energy Technology, Aalborg University, Aalborg, Denmark. From 2006 to 2007, he was with the Devision of Electronics and Information Engineering, Jeonbuk National University, Jeonju, Korea. In 2007, he joined the Department of Electrical and Computer Engineering, Ajou University, Suwon, Korea. He is Associate Editor of IEEE Transactions on Power Electronics. His research interests include electric machine drives, renewable power generations, and electric vehicle applications.
