Low-Resolution Fault Localization Using Phasor Measurement Units with Community Detection

TL;DR

This paper explores fault localization in power systems using limited PMU measurements, demonstrating how graph clustering techniques can improve fault detection at a sub-graph level when the system is unobservable.

Contribution

It introduces a novel approach leveraging community detection in network science to localize faults with low measurement data, filling a gap in existing observability-based methods.

Findings

Fault localization is feasible with scarce measurements.

Graph clustering enhances fault detection accuracy.

Localization is effective at the sub-graph level.

Abstract

A significant portion of the literature on fault localization assumes (more or less explicitly) that there are sufficient reliable measurements to guarantee that the system is observable. While several heuristics exist to break the observability barrier, they mostly rely on recognizing spatio-temporal patterns, without giving insights on how the performance are tied with the system features and the sensor deployment. In this paper, we try to fill this gap and investigate the limitations and performance limits of fault localization using Phasor Measurement Units (PMUs), in the low measurements regime, i.e., when the system is unobservable with the measurements available. Our main contribution is to show how one can leverage the scarce measurements to localize different type of distribution line faults (three-phase, single-phase to ground, ...) at the level of sub-graph, rather than with the resolution of a line. We show that the resolution we obtain is strongly tied with the graph clustering notion in network science.