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Detection of False Data Injection Attacks in a Smart Grid Based on WLS and an Adaptive Interpolation Extended Kalman Filter

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  • Guoqing Zhang

    (School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
    Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Chinese Ministry of Education, Wuhu 241000, China)

  • Wengen Gao

    (School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
    Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Chinese Ministry of Education, Wuhu 241000, China)

  • Yunfei Li

    (School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
    Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Chinese Ministry of Education, Wuhu 241000, China)

  • Xinxin Guo

    (School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
    Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Chinese Ministry of Education, Wuhu 241000, China)

  • Pengfei Hu

    (School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
    Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Chinese Ministry of Education, Wuhu 241000, China)

  • Jiaming Zhu

    (School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
    Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Chinese Ministry of Education, Wuhu 241000, China)

Abstract

An accurate power state is the basis of the normal functioning of the smart grid. However, false data injection attacks (FDIAs) take advantage of the vulnerability in the bad data detection mechanism of the power system to manipulate the process of state estimation. By attacking the measurements, then affecting the estimated state, FDIAs have become a serious hidden danger that affects the security and stable operation of the power system. To address the bad data detection vulnerability, in this paper, a false data attack detection method based on weighted least squares (WLS) and an adaptive interpolation extended Kalman filter (AIEKF) is proposed. On the basis of applying WLS and AIEKF, the Euclidean distance is used to calculate the deviation values of the two-state estimations to determine whether the current moment is subjected to a false data injection attack in the power system. Extensive experiments were conducted to simulate an IEEE-14-bus power system, showing that the adaptive interpolation extended Kalman filter can compensate for the deficiency in the bad data detection mechanism and successfully detect FDIAs.

Suggested Citation

  • Guoqing Zhang & Wengen Gao & Yunfei Li & Xinxin Guo & Pengfei Hu & Jiaming Zhu, 2023. "Detection of False Data Injection Attacks in a Smart Grid Based on WLS and an Adaptive Interpolation Extended Kalman Filter," Energies, MDPI, vol. 16(20), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7203-:d:1265155
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    References listed on IDEAS

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    1. Sepideh Radhoush & Trevor Vannoy & Kaveen Liyanage & Bradley M. Whitaker & Hashem Nehrir, 2023. "Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network," Energies, MDPI, vol. 16(5), pages 1-22, February.
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