Detection and Mitigation of False Data in Cooperative DC Microgrids With Unknown Constant Power Loads

Abstract

The rapid development and implementation of distributed control algorithms for dc microgrids has increased the vulnerability of this type of system to false data injection attacks, being one of the most prominent types of cyber attacks. This fact has motivated the development of different false data detection and impact mitigation strategies. A common approach for the detection is based on implementing an observer that can achieve a reliable estimation of the system states. However, approaches available in the literature assume that the underlying microgrid model is linear, which is generally not the case, specially when the dc microgrid supplies nonlinear constant power loads (CPLs). Consequently, this article proposes a distributed nonlinear observer approach that can robustly detect and reconstruct the applied false data attack in the dc microgrid's current sensors and cyber links, even in the presence of local unknown CPLs. First, the system is transformed into an observable form. Second, a high-order sliding-mode observer is implemented to estimate the system states and CPL, even in the presence of false data. Finally, the estimation is used to reconstruct the attack signal. The robustness of the proposed strategy is validated through numerical simulations and in an experimental prototype under measurement noise, uncertainty, and communication delays.