False Data Injection Attack Detection in DC Microgrids Based on Data-Driven Unknown Input Observers

DC 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.