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

arXiv: https://arxiv.org/abs/2205.13116 · HTML: https://mlsanity.com/paper/2205.13116/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 48 references that are in the map.
- First posted on arXiv 26 May 2022.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018)
- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)
- [The Impact of Random Models on Clustering Similarity](https://mlsanity.com/paper/1701.06508/) (2017)
- [Power System Event Identification based on Deep Neural Network with Information Loading](https://mlsanity.com/paper/2011.06718/) (2020)
- [Data-Driven Short-Term Voltage Stability Assessment Based on Spatial-Temporal Graph Convolutional Network](https://mlsanity.com/paper/2103.03729/) (2021)
- [Unsupervised Event Detection, Clustering, and Use Case Exposition in Micro-PMU Measurements](https://mlsanity.com/paper/2007.15237/) (2020)
- [Learning-Based Real-Time Event Identification Using Rich Real PMU Data](https://mlsanity.com/paper/2006.10121/) (2020)
- [Learning Latent Interactions for Event classification via Graph Neural Networks and PMU Data](https://mlsanity.com/paper/2010.01616/) (2020)
- [Robust Event Classification Using Imperfect Real-world PMU Data](https://mlsanity.com/paper/2110.10128/) (2021)

## Built on by (most cited first, 1 of 1)

- [DynamoPMU: A Physics Informed Anomaly Detection and Prediction Methodology using non-linear dynamics from μPMU Measurement Data](https://mlsanity.com/paper/2304.00092/) (2023)

## Abstract (arXiv)

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.