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Securing cyber-physical systems: Attack detection and isolation in power grid AGC

Author

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  • Abbasi, Muhammad Asim
  • Khan, Aadil Sarwar
  • Huang, Shiping
  • Qadri, Mansoor Zahoor
  • Guo, Li

Abstract

Modern power systems integrate cutting-edge communication and computational technologies with the physical infrastructure, making them a good example of a cyber–physical system (CPS). Like any other CPS, the power system is prone to cyber attacks, particularly in automatic generation control (AGC). AGC in the power system relies on the communication network and is therefore vulnerable to attacks. This paper studies the detection and isolation of multiple simultaneous attacks against multi-area AGC in the presence of renewable energy resources (RERs) and electric vehicles (EVs). The impact of EVs and RERs is modeled as unknown inputs/disturbances. A directional unknown input observer (DUIO) based approach is proposed to assure simultaneous disturbance decoupling and attack isolation with lesser computational burden than the existing schemes. The effectiveness of the proposed method is validated through comprehensive case studies and simulations on a two-area interconnected AGC power system, representing a typical multi-region power grid with renewable energy and electric vehicle integration.

Suggested Citation

  • Abbasi, Muhammad Asim & Khan, Aadil Sarwar & Huang, Shiping & Qadri, Mansoor Zahoor & Guo, Li, 2025. "Securing cyber-physical systems: Attack detection and isolation in power grid AGC," International Journal of Critical Infrastructure Protection, Elsevier, vol. 51(C).
    • Handle: RePEc:eee:ijocip:v:51:y:2025:i:c:s1874548225000678
    DOI: 10.1016/j.ijcip.2025.100806
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    References listed on IDEAS

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    1. Genge, Béla & Kiss, István & Haller, Piroska, 2015. "A system dynamics approach for assessing the impact of cyber attacks on critical infrastructures," International Journal of Critical Infrastructure Protection, Elsevier, vol. 10(C), pages 3-17.
    2. Zhang, Jiusi & Jiang, Yuchen & Li, Xiang & Huo, Mingyi & Luo, Hao & Yin, Shen, 2022. "An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
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