Robust Dynamic State Estimation of Power Systems demonstrates how to implement and apply robust dynamic state estimators to problems in modern power systems, thereby bridging the literatures of dynamic state estimation and robust estimation theory. The book presents Kalman filter algorithms, demonstrating how to build powerful, robust counterparts. Following sections build out case study-based implementations of robust Kalman filters to decontextualized applications across dynamic state estimation in power systems. Coverage encompasses theoretical backgrounds, motivations, problem formulation, implementations, uncertainties, anomalies and practical applications, such as generator parameter calibration, unknown inputs estimation, control failure detection, protection, and cyberattack detection.
Future research topics are identified and discussed, including open research questions. The book will serve as a key reference for power system real-time monitoring, control center engineers, and graduate students for learning (course related work) and research.
1. Chapter 1: Introduction - p. 1 2. Part 1: Robust estimation theory - p. P1 3. Chapter 2: Estimation theory - p. 7 4. Chapter 3: Parametric estimation theory - p. 41 5. Chapter 4: Generalized maximum likelihood (M-) estimators - p. 75 6. Chapter 5: Robustness concepts - p. 89 7. Part 2: Robust static and dynamic state estimators - p. P2 8. Chapter 6: Regression estimators - p. 115 9. Chapter 7: Kalman filter - p. 161 10. Chapter 8: Nonlinear filters - p. 181 11. Part 3: Robust power system dynamic state estimators - p. P3 12. Chapter 9: Modeling of power system dynamics - p. 231 13. Chapter 10: Observability analysis - p. 267 14. Chapter 11: Dynamic state estimation applications - p. 287 15. References - p. 313 16. Subject index - p. 323
Robust Dynamic State Estimation of Power Systems demonstrates how to implement and apply robust dynamic state estimators to problems in modern power systems, thereby bridging the literatures of dynamic state estimation and robust estimation theory. The book presents Kalman filter algorithms, demonstrating how to build powerful, robust counterparts. Following sections build out case study-based implementations of robust Kalman filters to decontextualized applications across dynamic state estimation in power systems. Coverage encompasses theoretical backgrounds, motivations, problem formulation, implementations, uncertainties, anomalies and practical applications, such as generator parameter calibration, unknown inputs estimation, control failure detection, protection, and cyberattack detection.
Future research topics are identified and discussed, including open research questions. The book will serve as a key reference for power system real-time monitoring, control center engineers, and graduate students for learning (course related work) and research.
1. Chapter 1: Introduction - p. 1 2. Part 1: Robust estimation theory - p. P1 3. Chapter 2: Estimation theory - p. 7 4. Chapter 3: Parametric estimation theory - p. 41 5. Chapter 4: Generalized maximum likelihood (M-) estimators - p. 75 6. Chapter 5: Robustness concepts - p. 89 7. Part 2: Robust static and dynamic state estimators - p. P2 8. Chapter 6: Regression estimators - p. 115 9. Chapter 7: Kalman filter - p. 161 10. Chapter 8: Nonlinear filters - p. 181 11. Part 3: Robust power system dynamic state estimators - p. P3 12. Chapter 9: Modeling of power system dynamics - p. 231 13. Chapter 10: Observability analysis - p. 267 14. Chapter 11: Dynamic state estimation applications - p. 287 15. References - p. 313 16. Subject index - p. 323
