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

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

doi:10.1109/tii.2018.2875529

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

Journal Article · Thu Oct 11 00:00:00 EDT 2018 · IEEE Transactions on Industrial Informatics

DOI:https://doi.org/10.1109/tii.2018.2875529· OSTI ID:1787209

^[1]; ^[1]; ^[2]; Zhao, Junbo ^[3]; ^[4]; Chen, Ying ^[1]

Tsinghua Univ., Beijing (China)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Falls Church, VA (United States)
Aalborg Univ. (Denmark)

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. In this work, 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.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); National Basic Research Program of China; China Postdoctoral Science Foundation; National Natural Science Foundation of China (NSFC)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1787209

Report Number(s):: LLNL-JRNL--795654; 989906

Journal Information:: IEEE Transactions on Industrial Informatics, Journal Name: IEEE Transactions on Industrial Informatics Journal Issue: 5 Vol. 15; ISSN 1551-3203

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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24 POWER TRANSMISSION AND DISTRIBUTION
cyber security
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Detecting False Data Injection Attacks Against Power System State Estimation With Fast Go-Decomposition Approach

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