An Effective Attack-Resilient Kalman Filter-Based Approach for Dynamic State Estimation of Synchronous Machine

Authors

  • A. A. Safavi Advanced Control Laboratory, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
  • Z. Kazemi Advanced Control Laboratory, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
Abstract:

Kalman filtering has been widely considered for dynamic state estimation in smart grids. Despite its unique merits, the Kalman Filter (KF)-based dynamic state estimation can be undesirably influenced by cyber adversarial attacks that can potentially be launched against the communication links in the Cyber-Physical System (CPS). To enhance the security of KF-based state estimation, in this paper, the basic KF-based method is enhanced by incorporating the dynamics of the attack vector into the system state-space model using an observer-based preprocessing stage. The proposed technique not only immunizes the state estimation against cyber-attacks but also effectively handles the issues relevant to the modeling uncertainties and measurement noises/errors. The effectiveness of the proposed approach is demonstrated by detailed mathematical analysis and testing it on two well-known IEEE cyber-physical test systems.

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Journal title

volume 16  issue 3

pages  279- 291

publication date 2020-09

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