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Detection of Stealthy False Data Injection Attacks in Modular Multilevel Converters

Author

Listed:
  • Xingxing Chen

    (Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China)

  • Shuguang Song

    (College of New Energy, China University of Petroleum (East China), Qingdao 266580, China)

Abstract

A modular multilevel converter (MMC) in a high-voltage direct-current (HVDC) transmission system consists of an electric-coupled physical system and a communication-coupled cyber system, leading to a cyber-physical system (CPS). Such a CPS is vulnerable to false data injection attacks (FDIA), which are the main category of cyberattacks. FDIAs can be launched by injecting false data into the control or communication system of the MMC to change the submodule (SM) capacitor voltage seen by the central controller. Consequently, the capacitor voltage of the attacked SM will deviate from its normal value and thus threaten the safe operation of the converter. Stealthy FDIAs characterized by elaborated attack sequences are more dangerous because they can deceive and bypass the attack detector presented in the existing literature for the MMC. To address this issue, this paper proposes a stealthy FDIA detection method to obtain the real SM capacitor voltages. Thus, the attacked SM can be located by comparing its real capacitor voltage with prespecified thresholds. Simulation results validate the effectiveness of the proposed detection and protection strategies.

Suggested Citation

  • Xingxing Chen & Shuguang Song, 2023. "Detection of Stealthy False Data Injection Attacks in Modular Multilevel Converters," Energies, MDPI, vol. 16(17), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6353-:d:1231256
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    References listed on IDEAS

    as
    1. Yumeng Tian & Harith R. Wickramasinghe & Zixin Li & Josep Pou & Georgios Konstantinou, 2022. "Review, Classification and Loss Comparison of Modular Multilevel Converter Submodules for HVDC Applications," Energies, MDPI, vol. 15(6), pages 1-32, March.
    2. Guanyuan Cheng & Shaojian Song, 2023. "Fault Detection and Identification in MMCs Based on DSCNNs," Energies, MDPI, vol. 16(8), pages 1-17, April.
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