Robust Dynamic State Estimation of Power Systems
Robust Dynamic State Estimation of Power Systems

Author: Junbo Zhao

Publisher: Elsevier

Published: 2023-06-01

Total Pages: 0

ISBN-13: 0323859070

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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. 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

Event-Trigger Dynamic State Estimation for Practical WAMS Applications in Smart Grid
Event-Trigger Dynamic State Estimation for Practical WAMS Applications in Smart Grid

Author: Zhen Li

Publisher: Springer Nature

Published: 2020-06-03

Total Pages: 294

ISBN-13: 3030456587

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This book describes how dynamic state estimation application in wide-area measurement systems (WAMS) are crucial for power system reliability, to acquire precisely power system dynamics. The event trigger DSE techniques described by the authors provide a design balance between the communication rate and estimation performance, by selectively sending the innovational data. The discussion also includes practical problems for smart grid applications, such as the non-Gaussian process/measurement noise, packet dropout, computation burden of accurate DSE, robustness to the system variation, etc. Readers will learn how the event trigger DSE can facilitate the effective reduction of communication rates, with guaranteed accuracy under a variety of practical conditions in smart grid applications.

Dynamic State Estimation in Power Systems
Dynamic State Estimation in Power Systems

Author: Hamed Tebianian

Publisher:

Published: 2014

Total Pages:

ISBN-13:

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Research in the area of power system transient stability has recently focused on dynamic state estimation using high rate Phasor Measurement Unit (PMU) data. Several mathematical models for synchronous machine are developed and various estimation approaches are proposed for this purpose. In this thesis, the mathematical formulation of nonlinear state space modeling and the principles of Kalman Filter are explained. Extended and Unscented Kalman Filters (EKF and UKF), as two nonlinear estimation methods, are applied for state and parameter estimation in an induction motor. In the next stage, after presenting a thorough explanation about modeling of the synchronous machine, dynamic state estimation is applied on different power system case studies and the results of estimation methods are compared. The simulation results provided in this thesis show the great potential of the proposed estimation approaches for accurately estimating the states of the machine as well as reducing the effect of noise on input signals.