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A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids

Author

Listed:
  • Omar A. Beg

    (Department of Electrical Engineering, The University of Texas Permian Basin, Odessa, TX 79762, USA
    These authors contributed equally to this work.)

  • Asad Ali Khan

    (Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA
    These authors contributed equally to this work.)

  • Waqas Ur Rehman

    (Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA)

  • Ali Hassan

    (Department of Electrical and Computer Engineering, University of Michigan, Dearborn, MI 48126, USA)

Abstract

In this paper, the application and future vision of Artificial Intelligence (AI)-based techniques in microgrids are presented from a cyber-security perspective of physical devices and communication networks. The vulnerabilities of microgrids are investigated under a variety of cyber-attacks targeting sensor measurements, control signals, and information sharing. With the inclusion of communication networks and smart metering devices, the attack surface has increased in microgrids, making them vulnerable to various cyber-attacks. The negative impact of such attacks may render the microgrids out-of-service, and the attacks may propagate throughout the network due to the absence of efficient mitigation approaches. AI-based techniques are being employed to tackle such data-driven cyber-attacks due to their exceptional pattern recognition and learning capabilities. AI-based methods for cyber-attack detection and mitigation that address the cyber-attacks in microgrids are summarized. A case study is presented showing the performance of AI-based cyber-attack mitigation in a distributed cooperative control-based AC microgrid. Finally, future potential research directions are provided that include the application of transfer learning and explainable AI techniques to increase the trust of AI-based models in the microgrid domain.

Suggested Citation

  • Omar A. Beg & Asad Ali Khan & Waqas Ur Rehman & Ali Hassan, 2023. "A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids," Energies, MDPI, vol. 16(22), pages 1-23, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7644-:d:1282816
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    References listed on IDEAS

    as
    1. Divya R. Nair & Manjula G. Nair & Tripta Thakur, 2022. "A Smart Microgrid System with Artificial Intelligence for Power-Sharing and Power Quality Improvement," Energies, MDPI, vol. 15(15), pages 1-20, July.
    2. 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.
    3. Mishra, Sakshi & Anderson, Kate & Miller, Brian & Boyer, Kyle & Warren, Adam, 2020. "Microgrid resilience: A holistic approach for assessing threats, identifying vulnerabilities, and designing corresponding mitigation strategies," Applied Energy, Elsevier, vol. 264(C).
    4. Smitha Joyce Pinto & Pierluigi Siano & Mimmo Parente, 2023. "Review of Cybersecurity Analysis in Smart Distribution Systems and Future Directions for Using Unsupervised Learning Methods for Cyber Detection," Energies, MDPI, vol. 16(4), pages 1-24, February.
    5. 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).
    6. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    Full references (including those not matched with items on IDEAS)

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