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Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids

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  • Dai Wang

    (Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China)

  • Xiaohong Guan

    (Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China)

  • Ting Liu

    (Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China)

  • Yun Gu

    (Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China)

  • Chao Shen

    (Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China)

  • Zhanbo Xu

    (Systems Engineering Institute, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China)

Abstract

False data injection (FDI) is considered to be one of the most dangerous cyber-attacks in smart grids, as it may lead to energy theft from end users, false dispatch in the distribution process, and device breakdown during power generation. In this paper, a novel kind of FDI attack, named tolerable false data injection (TFDI), is constructed. Such attacks exploit the traditional detector’s tolerance of observation errors to bypass the traditional bad data detection. Then, a method based on extended distributed state estimation (EDSE) is proposed to detect TFDI in smart grids. The smart grid is decomposed into several subsystems, exploiting graph partition algorithms. Each subsystem is extended outward to include the adjacent buses and tie lines, and generate the extended subsystem. The Chi-squares test is applied to detect the false data in each extended subsystem. Through decomposition, the false data stands out distinctively from normal observation errors and the detection sensitivity is increased. Extensive TFDI attack cases are simulated in the Institute of Electrical and Electronics Engineers (IEEE) 14-, 39-, 118- and 300-bus systems. Simulation results show that the detection precision of the EDSE-based method is much higher than that of the traditional method, while the proposed method significantly reduces the associated computational costs.

Suggested Citation

  • Dai Wang & Xiaohong Guan & Ting Liu & Yun Gu & Chao Shen & Zhanbo Xu, 2014. "Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids," Energies, MDPI, vol. 7(3), pages 1-22, March.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:3:p:1517-1538:d:33986
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    Citations

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    Cited by:

    1. Giacomo Valente & Vittoriano Muttillo & Mirco Muttillo & Gianluca Barile & Alfiero Leoni & Walter Tiberti & Luigi Pomante, 2019. "SPOF—Slave Powerlink on FPGA for Smart Sensors and Actuators Interfacing for Industry 4.0 Applications," Energies, MDPI, vol. 12(9), pages 1-13, April.
    2. Yazhou Jiang & Chen-Ching Liu & Yin Xu, 2016. "Smart Distribution Systems," Energies, MDPI, vol. 9(4), pages 1-20, April.
    3. Derya Betul Unsal & Taha Selim Ustun & S. M. Suhail Hussain & Ahmet Onen, 2021. "Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation," Energies, MDPI, vol. 14(9), pages 1-36, May.
    4. David Macii & Daniele Fontanelli & Grazia Barchi, 2020. "A Distribution System State Estimator Based on an Extended Kalman Filter Enhanced with a Prior Evaluation of Power Injections at Unmonitored Buses," Energies, MDPI, vol. 13(22), pages 1-25, November.
    5. Reda, Haftu Tasew & Anwar, Adnan & Mahmood, Abdun, 2022. "Comprehensive survey and taxonomies of false data injection attacks in smart grids: attack models, targets, and impacts," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    6. Zhengwei Qu & Jingchuan Yang & Yansheng Lang & Yunjing Wang & Xiaoming Han & Xinyue Guo, 2022. "Earth-Mover-Distance-Based Detection of False Data Injection Attacks in Smart Grids," Energies, MDPI, vol. 15(5), pages 1-16, February.
    7. Mihai Sanduleac & Gianluca Lipari & Antonello Monti & Artemis Voulkidis & Gianluca Zanetto & Antonello Corsi & Lucian Toma & Giampaolo Fiorentino & Dumitru Federenciuc, 2017. "Next Generation Real-Time Smart Meters for ICT Based Assessment of Grid Data Inconsistencies," Energies, MDPI, vol. 10(7), pages 1-16, June.
    8. Andrey Privalov & Vera Lukicheva & Igor Kotenko & Igor Saenko, 2019. "Method of Early Detection of Cyber-Attacks on Telecommunication Networks Based on Traffic Analysis by Extreme Filtering," Energies, MDPI, vol. 12(24), pages 1-14, December.
    9. Daniel Sousa-Dias & Daniel Amyot & Ashkan Rahimi-Kian & John Mylopoulos, 2023. "A Review of Cybersecurity Concerns for Transactive Energy Markets," Energies, MDPI, vol. 16(13), pages 1-32, June.
    10. Xuan Liu & Xingdong Liu & Zuyi Li, 2015. "Cyber Risk Assessment of Transmission Lines in Smart Grids," Energies, MDPI, vol. 8(12), pages 1-15, December.

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