Enhancing Resilience in Power Distribution Systems
Autor Fangxing Fran Li, Qingxin Shi, Jin Zhaoen Limba Engleză Paperback – 28 iul 2025
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.
- Breaks down novel methodologies and tools from deep learning to generative adversarial networks
- Supports readers in implementing practical steps towards resilient renewable energy
- Presents practical guidance for readers on the challenges and potential solutions for resilience in modern power systems
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Specificații
ISBN-13: 9780443236402
ISBN-10: 0443236402
Pagini: 232
Dimensiuni: 152 x 229 mm
Greutate: 0.42 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0443236402
Pagini: 232
Dimensiuni: 152 x 229 mm
Greutate: 0.42 kg
Editura: ELSEVIER SCIENCE
Cuprins
1. Resilience in Modern Distribution Systems
2. Solutions, Current Issues, and Future Challenges
3. Components in Distribution Systems
4. Resilience-Oriented Long-term Planning in Distribution systems
5. Resilience-Oriented Short-term Planning in Urban-Level Power Networks
6. Optimal Operation to Enhance Distribution Resilience
7. Machine Learning for Pre-Event Preparation
8. Machine Learning for During-Event Mitigation
9. Machine learning for post-event restoration
10. Conclusions
2. Solutions, Current Issues, and Future Challenges
3. Components in Distribution Systems
4. Resilience-Oriented Long-term Planning in Distribution systems
5. Resilience-Oriented Short-term Planning in Urban-Level Power Networks
6. Optimal Operation to Enhance Distribution Resilience
7. Machine Learning for Pre-Event Preparation
8. Machine Learning for During-Event Mitigation
9. Machine learning for post-event restoration
10. Conclusions
Notă biografică
Fangxing '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.