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OverviewEnhancing Resilience in Power Distribution Systems presents practical guidance for readers on the challenges and potential solutions for resilience in modern power systems. The book begins by explaining the risks and problems for resilience presented by renewable-based power systems. It goes on to clarify the current state of research and propose several novel methodologies and technologies for analysis and improvement of power system resilience. These methods include deep learning, linear programming, and generative adversarial networks. Packed with practical steps and tools for implementing the latest technologies, this book provides researchers and industry professionals with guidance on the resilient systems of the future. Full Product DetailsAuthor: Fangxing Fran Li (James McConnell Professor, Dept. of Electrical Engineering and Computer Science, University of Tennessee at Knoxville, USA) , Qingxin Shi (Assistant Professor, North China Electric Power University, Baoding, Hebei, China) , Jin Zhao (Alexander von Humboldt Researcher, Department of Electrical Engineering and Information Technology, Technical University Dortmund, Germany)Publisher: Elsevier - Health Sciences Division Imprint: Elsevier - Health Sciences Division Weight: 0.450kg ISBN: 9780443236402ISBN 10: 0443236402 Pages: 250 Publication Date: 01 July 2025 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: In Print ![]() This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of Contents1. Resilience in Modern Distribution Systems 2. Solutions, Current Issues, and Future Challenges 3. Components in Distribution Systems 4. Optimal Planning to Enhance Distribution Resilience 5. Optimal Operation to Enhance Distribution Resilience 6. Machine Learning Can Help Form Microgrids for Better Resilience 7. More on Machine Learning: When the Extreme Event Data is Scarce 8. ConclusionsReviewsAuthor InformationFangxing ‘Fran’ Li is the James W. McConnell Professor in Electrical Engineering and the Campus Director of CURENT at the University of Tennessee at Knoxville, USA. His current research interests include resilience, artificial intelligence in power, demand response, distributed generation and microgrid, and energy markets. From 2020 to 2021, he served as the Chair of the IEEE PES Power System Operation, Planning and Economics (PSOPE) Committee. He has been the Chair of IEEE WG on Machine Learning for Power Systems since 2019 and the Editor-In-Chief of IEEE Open Access Journal of Power and Energy (OAJPE) since 2020. Prof. Li has received numerous awards and honours including R&D 100 Award in 2020, IEEE PES Technical Committee Prize Paper award in 2019, 5 best or prize paper awards at international journals, and 6 best papers/posters at international conferences. Qingxin Shi is an Assistant Professor in the School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China. His research interests include demand response, resilient urban power systems, and hydrogen-electric integrated energy systems. He serves as the Associate Editor of Protection and Control of Modern Power Systems and the IEEE Open Access Journal of Power and Energy (OAJPE). Jin Zhao is an Assistant Professor in the Department of Electronic & Electrical Engineering, Trinity College Dublin, Ireland. Her research interests include power system resilience, climate adaptive energy systems, microgrids and machine learning. She currently serves as Senior Editor for IET Generation, Transmission & Distribution, Associate Editor for the IEEE Trans. on Smart Grid, Chair of the IEEE Task Force AISR, and as a member of the Steering Committee and PES representative for IEEE DataPort. Tab Content 6Author Website:Countries AvailableAll regions |