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False Data Injection Cyber-Attacks Detection for Multiple DC Microgrid Clusters

Author

Listed:
  • Tan, Sen
  • Xie, Peilin
  • Guerrero, Josep M.
  • Vasquez, Juan C.

Abstract

DC microgrids are considered as the next generation of power systems because of the possibility of connecting various renewable energy sources to different types of loads based on distributed networks. However, due to the strong reliance on communication networks, DC microgrids are vulnerable to intentional cyber-attacks. Therefore, in this paper, a robust cyber-attack detection scheme is proposed for DC microgrid systems. Utilizing the parity-based method, a multi-objective optimization problem is formulated to achieve robust detection against electrical parameter perturbations and unknown disturbances. An analytical solution is then provided using the singular value decomposition approach. With the disturbance decoupling scheme, the presented detection strategy can monitor the system with only local knowledge of the DC microgrid. The proposed method is easy to design and with less computation complexity. The performances of the provided scheme are validated by simulation tests and experimental results.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:310:y:2022:i:c:s0306261921016548
    DOI: 10.1016/j.apenergy.2021.118425
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    Citations

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    Cited by:

    1. Wenpei Li & Han Fu & Shun Wu & Bin Yang & Zhixiong Liu, 2023. "A Kalman Filter-Based Distributed Cyber-Attack Mitigation Strategy for Distributed Generator Units in Meshed DC Microgrids," Energies, MDPI, vol. 16(24), pages 1-20, December.
    2. Tostado-Véliz, Marcos & Hasanien, Hany M. & Rezaee Jordehi, Ahmad & Turky, Rania A. & Gómez-González, Manuel & Jurado, Francisco, 2023. "An Interval-based privacy – Aware optimization framework for electricity price setting in isolated microgrid clusters," Applied Energy, Elsevier, vol. 340(C).
    3. 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.
    4. 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.
    5. Xu, Junjun & Wu, Zaijun & Zhang, Tengfei & Hu, Qinran & Wu, Qiuwei, 2022. "A secure forecasting-aided state estimation framework for power distribution systems against false data injection attacks," Applied Energy, Elsevier, vol. 328(C).
    6. Xie, Peilin & Tan, Sen & Bazmohammadi, Najmeh & Guerrero, Josep. M. & Vasquez, Juan. C. & Alcala, Jose Matas & Carreño, Jorge El Mariachet, 2022. "A distributed real-time power management scheme for shipboard zonal multi-microgrid system," Applied Energy, Elsevier, vol. 317(C).
    7. Erdal Irmak & Ersan Kabalci & Yasin Kabalci, 2023. "Digital Transformation of Microgrids: A Review of Design, Operation, Optimization, and Cybersecurity," Energies, MDPI, vol. 16(12), pages 1-58, June.
    8. Li, Xueping & Wang, Yaokun & Lu, Zhigang, 2023. "Graph-based detection for false data injection attacks in power grid," Energy, Elsevier, vol. 263(PC).

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