Cyber-Physical Attack Conduction and Detection in Decentralized Power Systems

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Mohammadpourfard M., Weng Y., Khalili A., Genç V. M. I., Shefaei A., Mohammadi-Ivatloo B.

IEEE Access, vol.10, pp.29277-29286, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 10
  • Publication Date: 2022
  • Doi Number: 10.1109/access.2022.3151907
  • Journal Name: IEEE Access
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED)
  • Page Numbers: pp.29277-29286
  • Keywords: Power systems, State estimation, Power measurement, Optimization, Area measurement, Microgrids, Real-time systems, Deep learning, cyber-attacks, distributed state estimation, smart grids, DATA INJECTION ATTACKS, STATE ESTIMATION, SECURE ESTIMATION
  • Istanbul Technical University Affiliated: Yes


AuthorThe expansion of power systems over large geographical areas renders centralized processing inefficient. Therefore, the distributed operation is increasingly adopted. This work introduces a new type of attack against distributed state estimation of power systems, which operates on inter-area boundary buses. We show that the developed attack can circumvent existing robust state estimators and the convergence-based detection approaches. Afterward, we carefully design a deep learning-based cyber-anomaly detection mechanism to detect such attacks. Simulations conducted on the IEEE 14-bus system reveal that the developed framework can obtain a very high detection accuracy. Moreover, experimental results indicate that the proposed detector surpasses current machine learning-based detection methods.