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Blind False Data Injection Attacks Against State Estimation Based on Matrix Reconstruction.

Authors :
Yang, Haosen
He, Xing
Wang, Ziqiang
Qiu, Robert C.
Ai, Qian
Source :
IEEE Transactions on Smart Grid; Jul2022, Vol. 13 Issue 4, p3174-3187, 14p
Publication Year :
2022

Abstract

Cyber, especially false data injection attacks (FDIAs), are gradually becoming a kind of severe threat to modern power grids. This paper proposes a blind FDIA approach against the state estimation of power grids based on matrix reconstruction and subspace estimation. As a blind FDIA method, it requires no information about the system parameters and topology, but only needs a limited period of measurement data. In particular, this paper investigates the interference of random measurement noise for subspace based blind FDIA methods, and designs a scheme to alleviate the influence of this random noise in the process of FDIA. Compared with previous blind FDIA methods, the proposed method overcomes the shortcoming that measurement noise is difficult to address when data is not abundant. Thus the presented approach performs high successful rate of FDIA, and it is able to operate when measurement data is very limited. Besides, it performs great robustness to large-scale power grids and high-level measurement noise. Numerous cases demonstrate the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19493053
Volume :
13
Issue :
4
Database :
Complementary Index
Journal :
IEEE Transactions on Smart Grid
Publication Type :
Academic Journal
Accession number :
157618947
Full Text :
https://doi.org/10.1109/TSG.2022.3164874