A Data-Driven Framework for FDI Attack Detection and Mitigation in DC Microgrids

This paper proposes a Data-Driven (DD) framework for the real-time monitoring, detection, and mitigation of False Data Injection (FDI) attacks in DC Microgrids (DCMGs). A supervised algorithm is adopted this to continuously estimate output voltage current all Distributed Generators (DGs) with acceptable accuracy. Accordingly, among various evaluated DD algorithms, Adaptive Neuro-Fuzzy Inference Systems (ANFISs) are utilized because their low computational burden, efficiency operation, simplicity design implementation distributed control system. The proposed based on residual analysis generated error signal between estimated actual sensed signals. detects mitigates cyber-attack depending trends Moreover, by applying Online Change Point Detection (OCPD), need static user-defined threshold dispelled. Finally, method validated MATLAB/Simulink testbed, considering resilience, effectiveness, accuracy, robustness multiple case study scenarios.