IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v7y2014i3p1517-1538d33986.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/7/3/1517/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/7/3/1517/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Yazhou Jiang & Chen-Ching Liu & Yin Xu, 2016. "Smart Distribution Systems," Energies, MDPI, vol. 9(4), pages 1-20, April.
    8. 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.
    9. 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.
    10. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:7:y:2014:i:3:p:1517-1538:d:33986. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.