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 mat...

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Bibliographic Details
Published in:IEEE Energy Conversion Congress and Exposition pp. 796 - 801
Main Authors: Gajula, Kaushik, Le, Vu, Yao, Xiu, Zou, Shaofeng, Herrera, Luis
Format: Conference Proceeding
Language:English
Published: IEEE 10.10.2021
Subjects:
Admittance
Change detection algorithms
CUSUM
DC microgrid
Energy conversion
Fault detection
Kron reduction
Location awareness
Mathematical models
Microgrids
Quickest change detection
Series arc fault detection and localization
ISSN:2329-3748
Online Access:Get full text
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Summary: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.
ISSN:2329-3748
DOI:10.1109/ECCE47101.2021.9595315