Advanced Analytics for Reliability and Resilience of Energy Systems prepares students, researchers, and industry engineers to design and maintain reliable, sustainable energy systems using state-of-the-art AI techniques. This book provides a clear foundation in the fundamentals of power systems, statistics, and reliability, including resilience principles and strategies, practical applications, and real-world solutions. The AI tools presented range across forecasting, the Internet-of-Things, machine learning, digital twin technology, and big data analysis, with a variety of applications to avoid power outages, minimise disruption, and accurately assess system resilience. Including case studies and details methodology for practical techniques, Advanced Analytics for Reliability and Resilience of Energy Systems helps energy systems engineers and researchers to provide a stable and consistent power supply, in the face of climate change challenges and the energy transition
1. Introduction - p. xvii 2. Chapter 1 Enhancing resilience of power systems against typhoon threats: A hybrid data-model-driven approach - p. 1 3. Chapter 2 Reliability of power electronics in smart grids - p. 33 4. Chapter 3 Electricity distribution grids resilience enhancement by network reconfiguration - p. 60 5. Chapter 4 Teaching learning-based optimized artificial neural network for predicting the maximum power point of a large-scale grid-connected solar PV system - p. 93 6. Chapter 5 A reliable PV system implementation incorporating FPPT through forecasted load demand using neural network - p. 109 7. Chapter 6 Robust stabilization ellipsoidal design for normal and contingency operated power systems using Markov jump - p. 129 8. Chapter 7 From review to revolution: Innovative under-frequency load shedding for enhanced power system resilience - p. 153 9. Chapter 8 Fault detection and classification in hybrid AC/DC systems using artificial neural networks - p. 189 10. Chapter 9 A reliability constrained load balancing procedure for neutral current reduction in distribution systems - p. 227 11. Chapter 10 The impacts of cyber grid topology on the failure probability of transformers considering distributed generator controller - p. 266 12. Chapter 11 Hybrid renewable energy systems and microgrids - p. 288 13. Chapter 12 Resilient operation of power and gas networks to service restoration - p. 321 14. Chapter 13 Challenges and solutions in power-sharing control of microgrids with integrated renewable energy sources - p. 355 15. Chapter 14 Statistical assessment of supervisory control and data acquisition systems in photovoltaic power plant maintenance management - p. 385 16. Chapter 15 Small-signal stability analysis and compensation control for DC networked microgrid under multiple time delays - p. 395 17. Chapter 16 Multiplexing power networks with DC subgrid: Control strategy and economic impact assessment - p. 425 18. Index - p. 457
Advanced Analytics for Reliability and Resilience of Energy Systems prepares students, researchers, and industry engineers to design and maintain reliable, sustainable energy systems using state-of-the-art AI techniques. This book provides a clear foundation in the fundamentals of power systems, statistics, and reliability, including resilience principles and strategies, practical applications, and real-world solutions. The AI tools presented range across forecasting, the Internet-of-Things, machine learning, digital twin technology, and big data analysis, with a variety of applications to avoid power outages, minimise disruption, and accurately assess system resilience. Including case studies and details methodology for practical techniques, Advanced Analytics for Reliability and Resilience of Energy Systems helps energy systems engineers and researchers to provide a stable and consistent power supply, in the face of climate change challenges and the energy transition
1. Introduction - p. xvii 2. Chapter 1 Enhancing resilience of power systems against typhoon threats: A hybrid data-model-driven approach - p. 1 3. Chapter 2 Reliability of power electronics in smart grids - p. 33 4. Chapter 3 Electricity distribution grids resilience enhancement by network reconfiguration - p. 60 5. Chapter 4 Teaching learning-based optimized artificial neural network for predicting the maximum power point of a large-scale grid-connected solar PV system - p. 93 6. Chapter 5 A reliable PV system implementation incorporating FPPT through forecasted load demand using neural network - p. 109 7. Chapter 6 Robust stabilization ellipsoidal design for normal and contingency operated power systems using Markov jump - p. 129 8. Chapter 7 From review to revolution: Innovative under-frequency load shedding for enhanced power system resilience - p. 153 9. Chapter 8 Fault detection and classification in hybrid AC/DC systems using artificial neural networks - p. 189 10. Chapter 9 A reliability constrained load balancing procedure for neutral current reduction in distribution systems - p. 227 11. Chapter 10 The impacts of cyber grid topology on the failure probability of transformers considering distributed generator controller - p. 266 12. Chapter 11 Hybrid renewable energy systems and microgrids - p. 288 13. Chapter 12 Resilient operation of power and gas networks to service restoration - p. 321 14. Chapter 13 Challenges and solutions in power-sharing control of microgrids with integrated renewable energy sources - p. 355 15. Chapter 14 Statistical assessment of supervisory control and data acquisition systems in photovoltaic power plant maintenance management - p. 385 16. Chapter 15 Small-signal stability analysis and compensation control for DC networked microgrid under multiple time delays - p. 395 17. Chapter 16 Multiplexing power networks with DC subgrid: Control strategy and economic impact assessment - p. 425 18. Index - p. 457
