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Anomaly Detection in Power System State Estimation: Review and New Directions

Author

Listed:
  • Austin Cooper

    (Electrical and Computer Engineering Department, University of Florida, Gainesville, FL 32603, USA)

  • Arturo Bretas

    (Distributed Systems Group, Pacific Northwest National Laboratory, Richland, WA 99354, USA
    G2Elab, Grenoble INP, CNRS, Université Grenoble Alpes, 38000 Grenoble, France)

  • Sean Meyn

    (Electrical and Computer Engineering Department, University of Florida, Gainesville, FL 32603, USA)

Abstract

Foundational and state-of-the-art anomaly-detection methods through power system state estimation are reviewed. Traditional components for bad data detection, such as chi-square testing, residual-based methods, and hypothesis testing, are discussed to explain the motivations for recent anomaly-detection methods given the increasing complexity of power grids, energy management systems, and cyber-threats. In particular, state estimation anomaly detection based on data-driven quickest-change detection and artificial intelligence are discussed, and directions for research are suggested with particular emphasis on considerations of the future smart grid.

Suggested Citation

  • Austin Cooper & Arturo Bretas & Sean Meyn, 2023. "Anomaly Detection in Power System State Estimation: Review and New Directions," Energies, MDPI, vol. 16(18), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6678-:d:1242245
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    References listed on IDEAS

    as
    1. Mehdi Ganjkhani & Seyedeh Narjes Fallah & Sobhan Badakhshan & Shahaboddin Shamshirband & Kwok-wing Chau, 2019. "A Novel Detection Algorithm to Identify False Data Injection Attacks on Power System State Estimation," Energies, MDPI, vol. 12(11), pages 1-19, June.
    2. Aleksey S. Polunchenko & Vasanthan Raghavan, 2018. "Comparative performance analysis of the Cumulative Sum chart and the Shiryaev‐Roberts procedure for detecting changes in autocorrelated data," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 34(6), pages 922-948, November.
    3. George V. Moustakides & Aleksey S. Polunchenko & Alexander G. Tartakovsky, 2009. "Numerical Comparison of CUSUM and Shiryaev–Roberts Procedures for Detecting Changes in Distributions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 38(16-17), pages 3225-3239, October.
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