Volume 16, Issue 3 (September 2020)                   IJEEE 2020, 16(3): 279-291 | Back to browse issues page


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Kazemi Z, Safavi A A. An Effective Attack-Resilient Kalman Filter-Based Approach for Dynamic State Estimation of Synchronous Machine. IJEEE. 2020; 16 (3) :279-291
URL: http://ijeee.iust.ac.ir/article-1-1612-en.html
Abstract:   (357 Views)
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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  • The problem of the power system dynamic state estimation under cyber-attacks is studied in this paper.
  • A unique feature of the proposed method is its ability in the estimation of the attack vector.
  • By obtaining the dynamics of the attack vector using an unknown input observer, a new augmented system model is derived in this paper.
  • The effects of cyber-attacks, modeling uncertainties, and modeling noises are decoupled and separately considered in the proposed approach, which increases the estimation accuracy.
  • Detail mathematical analysis and several simulations demonstrate the effectiveness of the proposed method.

Type of Study: Research Paper | Subject: Phasor Measurement Unit (PMU) Systems
Received: 2019/09/25 | Revised: 2020/02/19 | Accepted: 2020/02/28

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© 2020 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.