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A Novel Detection Algorithm to Identify False Data Injection Attacks on Power System State Estimation

Author

Listed:
  • Mehdi Ganjkhani

    (Department of Electrical Engineering, Sharif University of Technology, Tehran P.O. Box 11365-11155, Iran)

  • Seyedeh Narjes Fallah

    (Independent Researcher, Sari 4816783787, Iran)

  • Sobhan Badakhshan

    (Department of Electrical Engineering, Sharif University of Technology, Tehran P.O. Box 11365-11155, Iran)

  • Shahaboddin Shamshirband

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

  • Kwok-wing Chau

    (Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China)

Abstract

This paper provides a novel bad data detection processor to identify false data injection attacks (FDIAs) on the power system state estimation. The attackers are able to alter the result of the state estimation virtually intending to change the result of the state estimation without being detected by the bad data processors. However, using a specific configuration of an artificial neural network (ANN), named nonlinear autoregressive exogenous (NARX), can help to identify the injected bad data in state estimation. Considering the high correlation between power system measurements as well as state variables, the proposed neural network-based approach is feasible to detect any potential FDIAs. Two different strategies of FDIAs have been simulated in power system state estimation using IEEE standard 14-bus test system for evaluating the performance of the proposed method. The results indicate that the proposed bad data detection processor is able to detect the false injected data launched into the system accurately.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2209-:d:238686
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    References listed on IDEAS

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    1. Zina Boussaada & Octavian Curea & Ahmed Remaci & Haritza Camblong & Najiba Mrabet Bellaaj, 2018. "A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation," Energies, MDPI, vol. 11(3), pages 1-21, March.
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    Cited by:

    1. 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.
    2. Virginia M. Romero & Eduardo B. Fernandez, 2023. "Towards a Reference Architecture for Cargo Ports," Future Internet, MDPI, vol. 15(4), pages 1-32, April.
    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. 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.
    5. 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.
    6. 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.
    7. 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).

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