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

Omar A. Beg (), Asad Ali Khan, Waqas Ur Rehman and Ali Hassan
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Omar A. Beg: Department of Electrical Engineering, The University of Texas Permian Basin, Odessa, TX 79762, USA
Asad Ali Khan: Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA
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

Energies, 2023, vol. 16, issue 22, 1-23

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.

Keywords: cyber-attacks; false data injection; microgrids; artificial intelligence; detection; mitigation; neural networks; smart grids (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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