Protection of large-scale smart grids against false data injection cyberattacks leading to blackouts

Javad Khazaei, M. Hadi Amini

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Power system is one of the most critical cyber–physical systems in a sustainable society, whose security and reliability can significantly impact other infrastructures. Therefore, security and resilience of smart grids are one of the most challenging research questions in protection of critical infrastructures. After cyberattacks on the U.S. power grid in 2018, the national security agency announced cyberattacks might cause a blackout in transmission systems. Therefore, it is critical to model cyberattack scenarios, propose a detection strategy to mitigate these threats in a realistic context, and study how the models can improve the defense of large-scale grids. In this paper, a bi-level mixed-integer linear programming (BMILP) model is developed to accurately model false data injections (FDIs) that are targeted to overflow multiple transmission lines and cause a blackout in large-scale grids. Compared to the existing research, the proposed model considers that attackers might have limited access to measurement buses and models attacks on targeted transmission lines without being detected by existing DC state-estimation. In addition, to protect the system against these attacks, it is proved that a detection framework based on recursive weighted least-square (WLS) state-estimation can detect the FDIs, unlike classical weighted least square (WLS) estimation that fails to identify stealthy FDIs. To validate the effectiveness of the proposed attack model and detection framework in practical grid infrastructures, the IEEE 118-bus benchmark and a 2000-bus synthetic grid replicating the electricity network of Texas, U.S. are used.

Original languageEnglish (US)
Article number100457
JournalInternational Journal of Critical Infrastructure Protection
Volume35
DOIs
StatePublished - Dec 2021

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Safety, Risk, Reliability and Quality
  • Computer Science Applications
  • Information Systems and Management

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