Detecting False Data Injection Attacks Against Power System State Estimation with Fast Go-Decomposition Approach

Boda Li, Tao Ding, Can Huang, Junbo Zhao, Yongheng Yang, Ying Chen

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7 Citationer (Scopus)
199 Downloads (Pure)

Abstrakt

State estimation is a fundamental function in modern energy management system, but its results may be vulnerable to false data injection attacks (FDIAs). FDIA is able to change the estimation results without being detected by the traditional bad data detection algorithms. In this paper, we propose an accurate and computational attractive approach for FDIA detection. We first rely on the low rank characteristic of the measurement matrix and the sparsity of the attack matrix to reformulate the FDIA detection as a matrix separation problem. Then, four algorithms that solve this problem are presented and compared, including the traditional augmented Lagrange multipliers (ALMs), double-noise-dual-problem (DNDP) ALM, the low rank matrix factorization, and the proposed new 'Go Decomposition (GoDec).' Numerical simulation results show that our GoDec algorithm outperforms the other three alternatives and demonstrates a much higher computational efficiency. Furthermore, GoDec is shown to be able to handle measurement noise and applicable for large-scale attacks.

OriginalsprogEngelsk
Artikelnummer8489956
TidsskriftI E E E Transactions on Industrial Informatics
Vol/bind15
Udgave nummer5
Sider (fra-til)2892-2904
Antal sider13
ISSN1551-3203
DOI
StatusUdgivet - maj 2019

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