Detection of false data injection attacks on power systems based on measurement-eigenvalue residual similarity test

Existing False data injection attack (FDIA) detection methods based on measurement similarity testing have difficulty in distinguishing between actual power grid accidents and FDIAs. Therefore, this paper proposes a detection method called the measurement-eigenvalue residual similarity (MERS) test,...

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Bibliographic Details
Published in:Frontiers in energy research 2023-11, Vol.11
Main Authors: Zhu, Yihua, Liu, Ren, Chang, Dongxu, Guo, Hengdao
Format: Article
Language:English
Subjects:
AC state estimation
bad data detection
cyber security
false data injection attacks
measurement-eigenvalue residual similarity
Citations: Items that this one cites
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Summary:Existing False data injection attack (FDIA) detection methods based on measurement similarity testing have difficulty in distinguishing between actual power grid accidents and FDIAs. Therefore, this paper proposes a detection method called the measurement-eigenvalue residual similarity (MERS) test, which can accurately detect FDIAs in AC state estimationof power system and effectively distinguish them from actual power grid accidents. Simulation results on the IEEE 39-bus system demonstrate that the proposed method achieves higher detection rates and lower false alarm rates than traditional methods under various operation conditions.
ISSN:2296-598X
2296-598X
DOI:10.3389/fenrg.2023.1285317