Angielski
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 in their classical form and sequentially demonstrates how to build their powerful robust counterparts. It then builds out case study-based implementations of robust Kalman filters to decontextualized applications across dynamic state estimation in power systems. Coverage encompasses the theoretical background of DSE, motivations, problem formulation, implementations, robustness enhancement to deal with uncertainties, anomalies and its related practical applications, such as generator parameter calibration, unknown inputs estimation, control failure detection, protection, and cyber-attack detection. Future research topics are identified and discussed, including open research questions. The book serves as a key reference for power system real-time monitoring, control center engineers, graduate students for learning (course related work) and research. 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 concept to practical implementations and applications Develops advanced robust statistics theory and uncertainty management methods
