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False Data Injection Attack Detection for Smart Grid Based on Square Root Unscented Kalman Filtering Estimate with Long Short Term Memory Correction

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Abstract

False Data Injection Attack (FDIA) severely damages the Power System State Estimation that ensures the safe operation of the Smart Grid. This paper proposes to compare the residuals between Square Root Unscented Kalman Filter (SRUKF) estimation and Weighted Least Squares (WLS) estimation with a given threshold value to detect FDIA. Considering that the SRUKF will approach to the wrong state as the attack continues, the Long Short Term Memory prediction model is deployed to correct the attack node state. Our method is compared with SRUKF, Extended Kalman Filter and other methods on IEEE-14 bus simulation experiments and the effectiveness of the proposed detection method has been verified.

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Acknowledgements

This work is supported by Yunnan Province Science and Technology Major Project (202302AD080002); Yunnan Fundamental Research Key Projects (202101AS070016); Yunnan Province Key Laboratory of Computer Technology Application Open Fund (CB22144S073A); Yunnan Province “Xingdian Talents Support Plan” Industrial Innovation Talents Project (Yfgr [2019] No.1096).

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Authors and Affiliations

  1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China

    Jing Zhang & Dong-Ming Ma

  2. Yunnan Key Laboratory of Artificial Intelligence, Kunming, 650500, China

    Jing Zhang & Dong-Ming Ma

  3. Yunnan Key Laboratory of Computer Technology Application, Kunming, 650500, China

    Jing Zhang & Dong-Ming Ma

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Zhang, J., Ma, DM. False Data Injection Attack Detection for Smart Grid Based on Square Root Unscented Kalman Filtering Estimate with Long Short Term Memory Correction. J. Electr. Eng. Technol. (2024). https://doi.org/10.1007/s42835-024-01850-7

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