@inproceedings{599a2be4399148bb935f6ebc14612d39,
title = "Quickest Detection of Series Arc Faults on DC Microgrids",
abstract = "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.",
keywords = "CUSUM, DC microgrid, Kron reduction, Quickest change detection, Series arc fault detection and localization",
author = "Kaushik Gajula and Vu Le and Xiu Yao and Shaofeng Zou and Luis Herrera",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 13th IEEE Energy Conversion Congress and Exposition, ECCE 2021 ; Conference date: 10-10-2021 Through 14-10-2021",
year = "2021",
doi = "10.1109/ECCE47101.2021.9595315",
language = "English",
series = "2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "796--801",
booktitle = "2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings",
address = "United States",
}