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

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 and infrequent, and have unknown characteristics. 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.