IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i18p6678-d1242245.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/18/6678/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/18/6678/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meng Xia & Dajun Du & Minrui Fei & Xue Li & Taicheng Yang, 2020. "A Novel Sparse Attack Vector Construction Method for False Data Injection in Smart Grids," Energies, MDPI, vol. 13(11), pages 1-19, June.
    2. Junhyung Bae, 2020. "Cost-Effective Placement of Phasor Measurement Units to Defend against False Data Injection Attacks on Power Grid," Energies, MDPI, vol. 13(15), pages 1-15, July.
    3. Shruti & Shalli Rani & Aman Singh & Reem Alkanhel & Dina S. M. Hassan, 2023. "SDAFA: Secure Data Aggregation in Fog-Assisted Smart Grid Environment," Sustainability, MDPI, vol. 15(6), pages 1-15, March.
    4. Raghuvamsi, Y & Teeparthi, Kiran, 2023. "A review on distribution system state estimation uncertainty issues using deep learning approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    5. Ameur, Hachmi Ben & Han, Xuyuan & Liu, Zhenya & Peillex, Jonathan, 2022. "When did global warming start? A new baseline for carbon budgeting," Economic Modelling, Elsevier, vol. 116(C).
    6. Virginia M. Romero & Eduardo B. Fernandez, 2023. "Towards a Reference Architecture for Cargo Ports," Future Internet, MDPI, vol. 15(4), pages 1-32, April.
    7. Derya Betul Unsal & Taha Selim Ustun & S. M. Suhail Hussain & Ahmet Onen, 2021. "Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation," Energies, MDPI, vol. 14(9), pages 1-36, May.
    8. Bitirgen, Kübra & Filik, Ümmühan Başaran, 2023. "A hybrid deep learning model for discrimination of physical disturbance and cyber-attack detection in smart grid," International Journal of Critical Infrastructure Protection, Elsevier, vol. 40(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6678-:d:1242245. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.