A Hybrid Framework for Detecting and Eliminating Cyber-Attacks in Power Grids

Arshia Aflaki, Mohsen Gitizadeh, Roozbeh Razavi-Far, Vasile Palade and Ali Akbar Ghasemi
Additional contact information
Arshia Aflaki: Department of Electronics and Electrical Engineering, Shiraz University of Technology, Shiraz 71555-313, Iran
Mohsen Gitizadeh: Department of Electronics and Electrical Engineering, Shiraz University of Technology, Shiraz 71555-313, Iran
Roozbeh Razavi-Far: Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada
Vasile Palade: Center for Data Science, Coventry University, Coventry CV1 5FB, UK
Ali Akbar Ghasemi: Department of Electronics and Electrical Engineering, Shiraz University of Technology, Shiraz 71555-313, Iran

Energies, 2021, vol. 14, issue 18, 1-22

Abstract: The work described in this paper aims to detect and eliminate cyber-attacks in smart grids that disrupt the process of dynamic state estimation. This work makes use of an unsupervised learning method, called hierarchical clustering, in an attempt to create an artificial sensor to detect two different cyber-sabotage cases, known as false data injection and denial-of-service, during the dynamic behavior of the power system. The detection process is conducted by using an unsupervised learning-enhanced approach, and a decision tree regressor is then employed for removing the threat. The dynamic state estimation of the power system is done by Kalman filters, which provide benefits in terms of the speed and accuracy of the process. Measurement devices in utilities and buses are vulnerable to communication interruptions between phasor measurement units and operators, who can be easily manipulated by false data. While Kalman filters are incapable of detecting the majority of such cyber-attacks, this article proves that the proposed unsupervised machine learning method is able to detect more than 90 percent of the mentioned attacks. The simulation results on the IEEE 9-bus with 3-machines and IEEE 14-bus with 5-machines systems verify the efficiency of the proposed approach.

Keywords: cyber-attacks; dynamic state estimation; hierarchical clustering; Kalman filter; unsupervised learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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