Journal of System Simulation
Abstract
Abstract: Due to the disruption and threat of false data injection attack (FDIA) on grid cyber-physical systems (GCPS), and to address the problem that false data is difficult to be detected, a method for smart grid false data detection based on weighted least squares (WLS) and improved unscented Kalman filter (UKF) is proposed. FDIA is modeled mathematically, and the residual analysis shows that the FDIA is difficult to be detected. In the case of the injection attack vector, the improved UKF is applied to state estimation. Meanwhile, the state estimation of the system is performed by the WLS, which is sensitive to the changes in the system. The results of the state estimation of the above two methods are used to execute a consistency test, and the situation of the FDIA is accurately determined based on the test results. Experimental analysis was conducted on the IEEE14 and IEEE57 systems and the detection rate was compared with the detection method of the support vector machine. The simulation results indicate that the FDIA can be detected accurately, thus the feasibility and effectiveness of the proposed method are demonstrated.
Recommended Citation
Wei, Lisheng and Zhang, Qian
(2023)
"Detection of False Data Injection Attack in Smart Grid Based on Improved UKF,"
Journal of System Simulation: Vol. 35:
Iss.
7, Article 8.
DOI: 10.16182/j.issn1004731x.joss.22-0292
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss7/8
First Page
1508
Last Page
1516
CLC
TM73; TP391
Recommended Citation
Wei Lisheng, Zhang Qian. Detection of False Data Injection Attack in Smart Grid Based on Improved UKF[J]. Journal of System Simulation, 2023, 35(7): 1508-1516.
DOI
10.16182/j.issn1004731x.joss.22-0292
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