Robust Dynamic State Estimation of Power Systems

Auteur
Junbo Zhao is an assistant professor of the Department of Electrical and Computer Engineering at University of Connecticut. He was an assistant professor at Mississippi State University from 2019-2021 and a research assistant professor at Virginia Tech from May 2018 till August 2019. He received the Ph.D. degree from Bradley Department of Electrical and Computer Engineering Virginia Tech, in 2018. He did the summer internship at Pacific Northwest National Laboratory in 2017. He is the Principal Investigator for a multitude of projects funded by the National Science Foundation, the Department of Energy, National Laboratories, and Eversource Energy. He is now the chair of IEEE Task Force on Power System Dynamic State and Parameter Estimation and IEEE Task Force on Cyber-Physical Interdependency for Power System Operation and Control, co-chair of the IEEE Working Group on Power System Static and Dynamic State Estimation, the secretary of IEEE PES Bulk Power System Operation Subcommittee and IEEE Task Force on Synchrophasor Applications in Power System Operation and Control. He has published three book chapters and more than 140 peer-reviewed journal and conference papers, where more than 70 appear in IEEE Transactions. He serves as the Associate Editor of IEEE Transactions on Power Systems, IEEE Transactions on Smart Grid, International Journal of Electrical Power & Energy Systems, Journal of Modern Power System and Clean Energy, and subject editor of IET Generation, Transmission & Distribution, and CSEE Journal of Power and Energy Systems. He has been listed as the 2020 World's Top 2% Scientists released by Stanford University. He is the receipt of the best paper awards of the 2020 and 2021 IEEE PES General Meeting (3 papers), the 2020 IEEE PES Outstanding Engineer Award, and the 2021 IEEE PES Outstanding Volunteer Award. 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 Federal University of Itajubá, Brazil, and the Ph.D. degree (2018) from Virginia Tech, USA, all in electrical engineering. His research interests include power system dynamics and stability, state estimation, and Koopman operator theory.Lamine Mili is currently a Professor of Electrical and Computer Engineering with Virginia Tech, Falls Church, VA, USA. He received the Electrical Engineering Diploma from the Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland, in 1976, and the Ph.D. degree from the University of Liège, Liège, Belgium, in 1987. He is an IEEE Life Fellow and the chair of IEEE Working Group on State Estimation Algorithms. His research has focused on power system resiliency and sustainability, risk management of complex systems, robust estimation and control, nonlinear dynamics.

Texte du rabat
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.

Contenu

1. Introduction2. State estimation theory3. Linear and Nonlinear Kalman Filtering4. Robust Kalman Filtering5. Power System Dynamics Modeling6. Observability Analysis7. Dynamic State Estimation Implementations8. Dynamic State Estimation Applications9. Transition to Future10. Conclusion11. Appendix