Robust Dynamic State Estimation of Power Systems
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.
2. State estimation theory
3. Linear and Nonlinear Kalman Filtering
4. Robust Kalman Filtering
5. Power System Dynamics Modeling
6. Observability Analysis
7. Dynamic State Estimation Implementations
8. Dynamic State Estimation Applications
9. Transition to Future
10. Conclusion11. Appendix
Assistant Professor at the New Jersey Institute of Technology, Newark, USA. He received the B.S. (2011) and the M.S. (2013) degrees from the Fed
- Elucidates theoretical motivations, definitions, formulations, and robustness enhancement
- Engages with emerging practical problems in the application of dynamic state estimation through case studies
- Provides a roadmap for the transition of DSE concepts to practical implementations and applications
- Develops advanced robust statistics theory and uncertainty management methods
Date de parution : 11-2024
Ouvrage de 256 p.
15x22.8 cm