Detection of false data injection attacks using unscented Kalman filter

It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems (EMSs), is vulnerable to false data injection attacks, due to the undetectability of those attacks using standard bad data detection techniques, which are typical...

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Bibliographic Details
Published in:Journal of modern power systems and clean energy 2018-09, Vol.6 (5), p.847-859
Main Authors: ŽIVKOVIĆ, Nemanja, SARIĆ, Andrija T.
Format: Article
Language:English
Subjects:
Algorithms
Bad data detection
Electrical Machines and Networks
Energy
Energy management systems
Energy Systems
False data injection attack
Kalman filters
Power Electronics
Real time
Renewable and Green Energy
Special Section on Cyber Physical Power Systems
State estimation
Time functions
Unscented Kalman filter
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Summary:It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems (EMSs), is vulnerable to false data injection attacks, due to the undetectability of those attacks using standard bad data detection techniques, which are typically based on normalized measurement residuals. Therefore, it is of the utmost importance to develop novel and efficient methods that are capable of detecting such malicious attacks. In this paper, we propose using the unscented Kalman filter (UKF) in conjunction with a weighted least square (WLS) based SE algorithm in real-time, to detect discrepancies between SV estimates and, as a consequence, to identify false data attacks. After an attack is detected and an appropriate alarm is raised, an operator can take actions to prevent or minimize the potential consequences. The proposed algorithm was successfully tested on benchmark IEEE 14-bus and 300-bus test systems, making it suitable for implementation in commercial EMS software.
ISSN:2196-5625
2196-5420
DOI:10.1007/s40565-018-0413-5