Quickest Detection of Series Arc Faults on DC Microgrids

In this paper we explore the problem of series arc fault detection and localization on dc microgrids. Through a statistical model of the microgrid obtained by nodal equation, the injection currents are modeled as a random vector whose distribution depends on the nodal voltages and the admittance matrix. A series arc fault causes a change in the admittance matrix, which further leads to a change in the data generating distribution of injection currents. The goal is to detect and localize faults on different lines in a timely fashion subject to false alarm constraints. The model is formulated as a quickest change detection problem, and the classical Cumulative Sum algorithm (CUSUM) is employed. The proposed framework is tested on a dc microgrid with active (constant power) loads. Furthermore, a case considering fault detection in the presence of an internal node is presented. Finally, we present an experimental result on a four node dc microgrid to verify the practical application of our approach.