Enhancing 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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Chapter 1. Resilience in modern distribution systems - p. 1 7. Chapter 2. Solutions, current issues, and future challenges - p. 19 8. Chapter 3. Components in distribution systems - p. 41 9. Chapter 4. Resilience-oriented long-term planning in distribution systems - p. 53 10. Chapter 5. Resilience-oriented short-term planning in urban-level power networks - p. 83 11. Chapter 6. Optimal operation to enhance distribution resilience - p. 107 12. Chapter 7. Machine learning for preparation before events - p. 139 13. Chapter 8. Machine learning for during-event mitigation - p. 165 14. Chapter 9. Machine learning for postevent restoration - p. 193 15. Chapter 10. Conclusions - p. 215 16. Index - p. 219
Enhancing 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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Chapter 1. Resilience in modern distribution systems - p. 1 7. Chapter 2. Solutions, current issues, and future challenges - p. 19 8. Chapter 3. Components in distribution systems - p. 41 9. Chapter 4. Resilience-oriented long-term planning in distribution systems - p. 53 10. Chapter 5. Resilience-oriented short-term planning in urban-level power networks - p. 83 11. Chapter 6. Optimal operation to enhance distribution resilience - p. 107 12. Chapter 7. Machine learning for preparation before events - p. 139 13. Chapter 8. Machine learning for during-event mitigation - p. 165 14. Chapter 9. Machine learning for postevent restoration - p. 193 15. Chapter 10. Conclusions - p. 215 16. Index - p. 219
