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
Preț: 893.82 lei
Preț vechi: 1233.61 lei
-28%
Puncte Express: 1341
Carte tipărită la comandă
Livrare economică 09-23 iulie
Livrare prin curier în România Termenul estimat este afișat lângă disponibilitate.
Transport gratuit pentru acest produs Plată online sau ramburs, în funcție de opțiunile comenzii.
Retur gratuit în 14 zile Comandă securizată și suport în română.
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