Fuzzy EKF-Based Intrusion Detection and Accurate State Estimation of Interconnected DC MGs With CPLs

With the advancement of digital technologies in complex and large-scale power systems, the issue of cyber-attack detection has become of paramount importance to maintain system reliability and performance. In this regard, accurate state estimation plays a key role and leads to proper supervisory decision-making. To detect cyber-attacks in direct current microgrids (DC MGs), a new centralized attack detection system based on the Kalman filter and clustering approach is proposed. On the other hand, appearing nonlinear loads such as constant power loads (CPLs) make the overall system behavior nonlinear and conventional linear Kalman filters ineffective. So, in this paper, an extended Kalman filter (EKF) is utilized. By constructing several output vectors and parallel EKFs, local state estimations are achieved. They are then used to detect attacks based on the residuals and localize them based on the fuzzy clustering approach. The local healthy estimations are aggregated to compute global estimations with low noise. The simulation results on a five-area interconnected DC MG subjected to data integrity and denial-of-service (DoS) attacks show that the proposed approach can detect attacks, evaluate how many attacks occur in the case of multiple attacks, localize the attacks, and obtain accurate state estimations.