Fusing Unscented Kalman Filter to Detect and Isolate Sensor Faults in DC Microgrids With CPLs

Abstract

Occurring faults in microgrids (MGs) and their installed sensors is dispensable and should be detected as fast as possible to prevent damages to the power system. This article investigates the problem of fault detection and isolation (FDI) in direct current (DC) MGs with nonlinear loads. A novel fusing unscented Kalman filter (UKF) for FDI is proposed to estimate the states of the system in the presence of faults. The proposed approach groups the multi-sensors of the system into several local measurements and local UKFs estimate the system states. Then, the local estimations are analyzed in the information mixture block to evaluate the faults and compute fault-free state estimations. Compared with state-of-the-art methods, the proposed approach offers a low computational burden, is applicable for nonlinear systems and with multi-sensors and multi-faults, and offers a systematic approach to isolate faulty sensors. The results show that the proposed algorithm has a good performance in fault detection and isolating it and outperforms the recent methods.