Cyber-Physical Attack Conduction and Detection in Decentralized Power Systems


Creative Commons License

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

IEEE Access, 2022 (Journal Indexed in SCI Expanded) identifier

  • Publication Type: Article / Article
  • Publication Date: 2022
  • Doi Number: 10.1109/access.2022.3151907
  • Title of Journal : IEEE Access
  • Keywords: Area measurement, cyber-attacks, Deep learning, distributed state estimation, Microgrids, Optimization, Power measurement, Power systems, Real-time systems, smart grids, State estimation

Abstract

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.