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A Data-Driven Framework for FDI Attack Detection and Mitigation in DC Microgrids

Author

Listed:
  • Amir Basati

    (Center for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg, Denmark)

  • Josep M. Guerrero

    (Center for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg, Denmark)

  • Juan C. Vasquez

    (Center for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg, Denmark)

  • Najmeh Bazmohammadi

    (Center for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg, Denmark)

  • Saeed Golestan

    (Center for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg, Denmark)

Abstract

This paper proposes a Data-Driven (DD) framework for the real-time monitoring, detection, and mitigation of False Data Injection (FDI) attacks in DC Microgrids (DCMGs). A supervised algorithm is adopted in this framework to continuously estimate the output voltage and current for all Distributed Generators (DGs) with acceptable accuracy. Accordingly, among the various evaluated supervised DD algorithms, Adaptive Neuro-Fuzzy Inference Systems (ANFISs) are utilized because of their low computational burden, efficiency in operation, and simplicity in design and implementation in a distributed control system. The proposed framework is based on the residual analysis of the generated error signal between the estimated and actual sensed signals. The proposed framework detects and mitigates the cyber-attack depending on trends in generated error signals. Moreover, by applying Online Change Point Detection (OCPD), the need for a static user-defined threshold for the residual analysis of the generated error signal is dispelled. Finally, the proposed method is validated in a MATLAB/Simulink testbed, considering the resilience, effectiveness, accuracy, and robustness of multiple case study scenarios.

Suggested Citation

  • Amir Basati & Josep M. Guerrero & Juan C. Vasquez & Najmeh Bazmohammadi & Saeed Golestan, 2022. "A Data-Driven Framework for FDI Attack Detection and Mitigation in DC Microgrids," Energies, MDPI, vol. 15(22), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8539-:d:973249
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    References listed on IDEAS

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    1. 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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