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Static Detection of False Data in the Power Grid by Fusing Structure and Attributes of Node

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Abstract

False data injection attack can evade the traditional state estimation in the power system, resulting in the historical data may have been polluted. Under such circumstances, the contaminated historical data cannot provide the priori data, so data-driven detection cannot be carried out. Hence, this paper proposes a static detection method of false data based on the similarity characteristics of network nodes at a certain time, where structure and attributes of nodes are fused to express nodes based on the egonet model of power grid. In addition, to improve the accuracy of clustering, the detection rate is adopted in the clustering method. The method is tested in IEEE118-bus and 2383-bus systems. The simulation results show that proposed method is effective, and can detect the possible false data injection problems in the power system over 80%.

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Acknowledgements

The authors are very grateful to the helpful comments from the editors and reviewers which significantly improved the quality of this paper. This work is supported in part by National Natural Science Foundation of China (No. 61473246) and Natural Science Foundation of Hebei Province (No. E2021203004).

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  1. Key Lab of Power Electronics for Energy Conservation and Motor Drive of Hebei Province, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Xueping Li, Xuanxuan Li & Zhigang Lu

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  1. Xueping Li
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Li, X., Li, X. & Lu, Z. Static Detection of False Data in the Power Grid by Fusing Structure and Attributes of Node. J. Electr. Eng. Technol. 18, 4079–4090 (2023). https://doi.org/10.1007/s42835-023-01494-z

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