Springer Application of Machine Learning and Deep Learning Methods to Power System Problems

This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses.  From the Back Cover This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses.  Offers innovative machine learning and deep learning methods for dealing with power system issues; Provides promising solution methodologies; Covers theoretical background and experimental analysis. About the Author Morteza Nazari-Heris, PhD,  is a Research Assistant in the Department of Architectural Engineering at The Pennsylvania State University. He obtained BSc and MSc degrees in electrical engineering from the University of Tabriz. His main areas of interest are micro grids, smart grids, integrated heat and power networks, and energy storage technologies. He has received several awards and fellowships, including outstanding national master and PhD student, selection among the top-rated applicants from the College of Engineering in recognition of the strength of academic record, three Outstanding Thesis Awards, and selection as IEEE PES DAY 2021 Ambassador in the IEEE Section Category from Central Pennsylvania Section. He also was selected as the Architectural Engineering Outstanding Graduate Student Awards and also received the Borda Graduate Scholarship in Honor of Gifford H. Albright-Scholarly Excellence. He serves as an editor and reviewer for a number of journals and symposia and is an active member of professional communities like Institute of Electrical and Electronics Engineers (IEEE), Clean Energy Leadership Institute, and Young Professionals in Energy. Somayeh Asadi, PhD, is an Associate Professor in the Department of Architectural Engineering at the Pennsylvania State University. She is also a Fulbright Specialist. Prior to that, she was an Assistant Professor at the Texas A&M University. She specializes in sustainable building and built environment, integrated building design systems, energy management, renewable energy systems, and smart grids. To date, her research efforts have resulted in more than 80 peer-reviewed journal articles, more than 10 book chapters, two books, and more than 60 peer-reviewed conference papers. Her research efforts include 12 externally funded projects as a PI and Co-PI, including 3 from the National Science Foundation, 3 from the Pennsylvania Department of Environmental Protection, 3 from the US Department of Energy, and 3 from Qatar National Research Foundation, resulting in more than $5 million in research project funding in total. She specializes in sustainable building and built environment, integrated building design systems, energy management, renewable energy systems, and smart grids. Behnam M