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This paper is concerned with the complex task of identifying the type andcause of the events that are captured by distribution-level phasor measurementunits (D-PMUs) in order to enhance situational awareness in power distributionsystems. 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 aboutthe event signatures due to the fact that the events are diverse, infrequent,and inherently unscheduled. To tackle these challenges, we propose anunsupervised graph-representation learning method, called GraphPMU, tosignificantly improve the performance in event clustering underlocationally-scarce data availability by proposing the following two newdirections: 1) using the topological information about the relative location ofthe few available phasor measurement units on the graph of the powerdistribution network; 2) utilizing not only the commonly used fundamentalphasor measurements, bus also the less explored harmonic phasor measurements inthe process of analyzing the signatures of various events. Through a detailedanalysis of several case studies, we show that GraphPMU can highly outperformthe prevalent methods in the literature.

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