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The main purpose of this article is to design a stealthy false data injection (FDI) attack and corresponding scalable detection mechanism for DC Microgrids. Firstly, a DC microgrid model is established that includes disturbances, measurement noise, and FDI attacks. Then, a novel FDI attack is designed to disrupt voltage balance and evade detectors based on unknown input observers (UIOs). Subsequently, a scalable detection mechanism combining distributed observers and UIOs is proposed to detect the novel attack, which does not rely on global system information (such as fixed topology) and therefore does not require global redesign when DGUs are plugged and unplugged. Finally, the theoretical results are verified by the SimPowerSystems Toolbox.

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Stealthy False Data Injection Attacks Design and Scalable Detection in DC Microgrids

  • 会议论文

  • 发表日期：

    2025年

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  • Zhihua Wu,
  • Chen Peng,
  • Engang Tian

摘要

The main purpose of this article is to design a stealthy false data injection (FDI) attack and corresponding scalable detection mechanism for DC Microgrids. Firstly, a DC microgrid model is established that includes disturbances, measurement noise, and FDI attacks. Then, a novel FDI attack is designed to disrupt voltage balance and evade detectors based on unknown input observers (UIOs). Subsequently, a scalable detection mechanism combining distributed observers and UIOs is proposed to detect the novel attack, which does not rely on global system information (such as fixed topology) and therefore does not require global redesign when DGUs are plugged and unplugged. Finally, the theoretical results are verified by the SimPowerSystems Toolbox.