PPGN: Physics-Preserved Graph Networks for Real-Time Fault Location in Distribution Systems with Limited Observation and Labels

Abstract

Electrical faults may trigger blackouts or wildfires without timely monitoring and control strategy. Traditional solutions for locating faults in distribution systems are not real-time when network observability is low, while novel black-box machine learning methods are vulnerable to stochastic environments. We propose a novel Physics-Preserved Graph Network (PPGN) architecture to accurately locate faults at the node level with limited observability and labeled training data. PPGN has a unique two-stage graph neural network architecture. The first stage learns the graph embedding to represent the entire network using a few measured nodes. The second stage finds relations between the labeled and unlabeled data samples to further improve the location accuracy. We explain the benefits of the two-stage graph configuration through a random walk equivalence. We numerically validate the proposed method in the IEEE 123-node and 37-node test feeders, demonstrating the superior performance over three baseline classifiers when labeled training data is limited, and loads and topology are allowed to vary.

10 pages, 4 figure

References in corpus (4)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Unifying Graph Convolutional Neural Networks and Label Propagation
• Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
• Physics-Informed Learning for High Impedance Faults Detection

Cited by in corpus (1)

• A Heterogeneous Graph-Based Multi-Task Learning for Fault Event Diagnosis in Smart Grid