GraphPMU: Event Clustering via Graph Representation Learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements

TL;DR

This paper introduces GraphPMU, an unsupervised graph learning approach that enhances event clustering in power distribution systems by leveraging limited D-PMU data and both fundamental and harmonic measurements.

Contribution

It proposes a novel graph-based method that uses topological info and harmonic measurements to improve event clustering with scarce measurement data.

Findings

GraphPMU significantly outperforms existing methods in case studies.

Utilizing harmonic measurements improves event signature analysis.

Topological information enhances clustering accuracy in sparse measurement scenarios.

Abstract

This paper is concerned with the complex task of identifying the type and cause of the events that are captured by distribution-level phasor measurement units (D-PMUs) in order to enhance situational awareness in power distribution systems. Our goal is to address two fundamental challenges in this field: a) scarcity in measurement locations due to the high cost of purchasing, installing, and streaming data from D-PMUs; b) limited prior knowledge about the event signatures due to the fact that the events are diverse, infrequent, and inherently unscheduled. To tackle these challenges, we propose an unsupervised graph-representation learning method, called GraphPMU, to significantly improve the performance in event clustering under locationally-scarce data availability by proposing the following two new directions: 1) using the topological information about the relative location of the few available phasor measurement units on the graph of the power distribution network; 2) utilizing not only the commonly used fundamental phasor measurements, bus also the less explored harmonic phasor measurements in the process of analyzing the signatures of various events. Through a detailed analysis of several case studies, we show that GraphPMU can highly outperform the prevalent methods in the literature.