scholarly journals Comparing Kalman Filters and Observers for Power System Dynamic State Estimation With Model Uncertainty and Malicious Cyber Attacks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 77155-77168 ◽  
Author(s):  
Junjian Qi ◽  
Ahmad F. Taha ◽  
Jianhui Wang
Keyword(s):  
Power System ◽  
State Estimation ◽  
Model Uncertainty ◽  
Kalman Filters ◽  
Cyber Attacks ◽  
System Dynamic ◽  
Dynamic State ◽  
Dynamic State Estimation

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

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5823
Author(s):  
Arshia Aflaki ◽  
Mohsen Gitizadeh ◽  
Roozbeh Razavi-Far ◽  
Vasile Palade ◽  
Ali Akbar Ghasemi
Keyword(s):  
Power System ◽  
State Estimation ◽  
Unsupervised Learning ◽  
Kalman Filters ◽  
Smart Grids ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Dynamic State ◽  
Learning Method ◽  
Dynamic State Estimation

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.

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