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A Novel False Measurement Data Detection Mechanism for Smart Grids

Author

Listed:
  • Muhammad Awais Shahid

    (Department of Electrical & Computer Engineering, Air University, Islamabad 44230, Pakistan)

  • Fiaz Ahmad

    (Department of Electrical & Computer Engineering, Air University, Islamabad 44230, Pakistan)

  • Rehan Nawaz

    (Department of Electrical & Computer Engineering, Air University, Islamabad 44230, Pakistan)

  • Saad Ullah Khan

    (Department of Electrical & Computer Engineering, Air University, Islamabad 44230, Pakistan)

  • Abdul Wadood

    (Department of Electrical Engineering, Air University Islamabad, Kamra Campus, Kamra 43570, Pakistan)

  • Hani Albalawi

    (Department of Electrical Engineering, Faculty of Engineering, University of Tabuk, Tabuk 47913, Saudi Arabia
    Renewable Energy and Energy Efficiency Center (REEEC), University of Tabuk, Tabuk 71491, Saudi Arabia)

Abstract

With the growing cyber-infrastructure of smart grids, the threat of cyber-attacks has intensified, posing an increased risk of compromised communication links. Of particular concern is the false data injection (FDI) attack, which has emerged as a highly dangerous cyber-attack targeting smart grids. This paper addresses the limitations of the variable dummy value model proposed in the authors previous work and presents a novel defense methodology called the nonlinear function-based variable dummy value model for the AC power flow network. The proposed model is evaluated using the IEEE 14-bus test system, demonstrating its effectiveness in detecting FDI attacks. It has been shown that previous detection techniques are unable to detect FDI attacks, whereas the proposed method is shown to be successful in the detection of such attacks, guaranteeing the security of the smart grid’s measurement infrastructure.

Suggested Citation

  • Muhammad Awais Shahid & Fiaz Ahmad & Rehan Nawaz & Saad Ullah Khan & Abdul Wadood & Hani Albalawi, 2023. "A Novel False Measurement Data Detection Mechanism for Smart Grids," Energies, MDPI, vol. 16(18), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6614-:d:1239669
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    References listed on IDEAS

    as
    1. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
    2. Sepideh Radhoush & Trevor Vannoy & Kaveen Liyanage & Bradley M. Whitaker & Hashem Nehrir, 2023. "Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network," Energies, MDPI, vol. 16(5), pages 1-22, February.
    3. Muhammad Awais Shahid & Fiaz Ahmad & Fahad R. Albogamy & Ghulam Hafeez & Zahid Ullah, 2022. "Detection and Prevention of False Data Injection Attacks in the Measurement Infrastructure of Smart Grids," Sustainability, MDPI, vol. 14(11), pages 1-25, May.
    4. Li, Xueping & Wang, Yaokun & Lu, Zhigang, 2023. "Graph-based detection for false data injection attacks in power grid," Energy, Elsevier, vol. 263(PC).
    5. Ajit Kumar & Neetesh Saxena & Souhwan Jung & Bong Jun Choi, 2021. "Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling," Energies, MDPI, vol. 15(1), pages 1-22, December.
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