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Not AvailableDC microgrid systems commonly feature a hierarchical control architecture with multiple interconnected distributed generation units (DGUs), requiring the integration of communication layers. This integration introduces a potential vulnerability, as malicious attackers can exploit the system by injecting false data, which could result in a shift in the operating point of the system or make the entire system unstable. To overcome this issue, this article proposes a data-driven unknown input observer (UIO) to detect and identify false data injection attacks (FDIAs) in the system. The data-driven UIOs are designed using only historical input/output data, which can be collected through simulations or experimental results. The developed UIOs do not require knowledge of the microgrid parameters. The proposed data-driven UIOs are then validated through Simulink and hardware-in-the-loop real-time simulation case studies to detect FDIAs in the secondary control of dc microgrids. The results show that the proposed observers can effectively detect and localize FDIAs in the communication links of the system.
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An Ergodic CuSum Algorithm for False Data Injection Attacks Detection in DC Microgrids
DC microgrids have widely adopted hierarchical control architecture through distributed generation units (DGUs) to enhance reliability and scalability. However, this makes the system vulnerable to false data injection attacks (FDIAs), which can disrupt system stability or shift the operating point. While observers are commonly used to detect FDIAs, some FDIAs can be stealthy, or observers lack sufficient sensitivity for reliable identification. To address this, we propose a quickest change detection (QCD) method based on an unknown input observer (UIO) estimation error model to detect the FDIAs that are stealthy to the UIOs. The Ergodic CuSum algorithm is designed and can be efficiently updated using estimation error observations. The approach is validated through Simulink and real-time simulations.
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- Award ID(s):
- 2339434
- PAR ID:
- 10663027
- Publisher / Repository:
- IEEE
- Date Published:
- Page Range / eLocation ID:
- 1 to 7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1109/ECCE58356.2025.11260430
