  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.
