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Measurement protection to prevent cyber–physical attacks against power system State Estimation

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  • Margossian, Harag
  • Kfouri, Ronald
  • Saliba, Rita

Abstract

Smart applications supporting modern power systems are susceptible to cyber–physical attacks, particularly False Data Injection attacks that manipulate the input measurements of State Estimation (SE) compromising its output states. This paper proposes an Integer Linear Programming formulation that protects an optimal number of measurement units to prevent cyber–physical attacks, enhancing the robustness of SE. Our approach exhibits low complexity, applies to both linear and nonlinear SE, and converges rapidly toward the optimal solution. The formulation requires information about the grid topology and measurement distribution but does not depend on the power flow equations. Also, the generalized formulation can be customized to consider distinct protection costs for all measurement types, various priorities for different measurements, and a range of measurements and pseudo-measurements. Simulations are performed on the widely used IEEE 14 and 118-bus systems to verify the approach for linear and nonlinear SE and illustrate its practicality.

Suggested Citation

  • Margossian, Harag & Kfouri, Ronald & Saliba, Rita, 2023. "Measurement protection to prevent cyber–physical attacks against power system State Estimation," International Journal of Critical Infrastructure Protection, Elsevier, vol. 43(C).
    • Handle: RePEc:eee:ijocip:v:43:y:2023:i:c:s1874548223000562
    DOI: 10.1016/j.ijcip.2023.100643
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    References listed on IDEAS

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    1. Ahmad, Fiaz & Rasool, Akhtar & Ozsoy, Emre & Sekar, Raja & Sabanovic, Asif & Elitaş, Meltem, 2018. "Distribution system state estimation-A step towards smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2659-2671.
    2. Khazaei, Javad & Amini, M. Hadi, 2021. "Protection of large-scale smart grids against false data injection cyberattacks leading to blackouts," International Journal of Critical Infrastructure Protection, Elsevier, vol. 35(C).
    3. Majidi, Seyed Hossein & Hadayeghparast, Shahrzad & Karimipour, Hadis, 2022. "FDI attack detection using extra trees algorithm and deep learning algorithm-autoencoder in smart grid," International Journal of Critical Infrastructure Protection, Elsevier, vol. 37(C).
    4. Tan, Sen & Xie, Peilin & Guerrero, Josep M. & Vasquez, Juan C., 2022. "False Data Injection Cyber-Attacks Detection for Multiple DC Microgrid Clusters," Applied Energy, Elsevier, vol. 310(C).
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