Fault Detection and Isolation in DC Microgrids Based on Singularity Detection in the Second Derivative of Local Current Measurement

Abstract

DC microgrids are becoming an attractive technology for future power grids. However, the fault detection and isolation (FDI) schemes for dc microgrids are still in the nascent state. This article presents an FDI method for dc microgrids using the regularity information of the second derivative of current (SDOC). As the major contribution of this article, the coherence between the singular feature in the SDOC and the short-circuit fault in dc lines is first proved and applied to FDI. Furthermore, a singularity detection approach using stationary wavelet transform (SWT) is introduced. With this FDI method, not only the fault isolation but also the fault-type classification can be achieved based on only local current measurements. Moreover, compared with other local current-based methods, this method has a distinct advantage in the robustness against the nonfault disturbances. The effectiveness of this FDI method was verified through hardware tests under the real-time (RT) simulation of various fault scenarios in a 5-kV three-terminal dc microgrid model based on dual-active-bridge (DAB) converters. This FDI method can be generalized to dc microgrids with different topologies and converters.