Event Analysis of Pulse-reclosers in Distribution Systems Through Sparse Representation

TL;DR

This paper presents a novel method using sparse representation to analyze pulse-recloser events in distribution systems, enabling fault detection and classification with high accuracy from field data.

Contribution

The paper introduces a sparse representation-based algorithm for online event analysis and fault classification of pulse-reclosers in distribution systems, validated with real field data.

Findings

Effective fault detection and classification using pulse signatures

High accuracy verified with real distribution system data

Enhanced understanding of pulse signature characteristics

Abstract

The pulse-recloser uses pulse testing technology to verify that the line is clear of faults before initiating a reclose operation, which significantly reduces stress on the system components (e.g. substation transformers) and voltage sags on adjacent feeders. Online event analysis of pulse-reclosers are essential to increases the overall utility of the devices, especially when there are numerous devices installed throughout the distribution system. In this paper, field data recorded from several devices were analyzed to identify specific activity and fault locations. An algorithm is developed to screen the data to identify the status of each pole and to tag time windows with a possible pulse event. In the next step, selected time windows are further analyzed and classified using a sparse representation technique by solving an l1-regularized least-square problem. This classification is obtained by comparing the pulse signature with the reference dictionary to find a set that most closely matches the pulse features. This work also sheds additional light on the possibility of fault classification based on the pulse signature. Field data collected from a distribution system are used to verify the effectiveness and reliability of the proposed method.