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Electrical faults may trigger blackouts or wildfires without timelymonitoring and control strategy. Traditional solutions for locating faults indistribution systems are not real-time when network observability is low, whilenovel black-box machine learning methods are vulnerable to stochasticenvironments. We propose a novel Physics-Preserved Graph Network (PPGN)architecture to accurately locate faults at the node level with limitedobservability and labeled training data. PPGN has a unique two-stage graphneural network architecture. The first stage learns the graph embedding torepresent the entire network using a few measured nodes. The second stage findsrelations between the labeled and unlabeled data samples to further improve thelocation accuracy. We explain the benefits of the two-stage graph configurationthrough a random walk equivalence. We numerically validate the proposed methodin the IEEE 123-node and 37-node test feeders, demonstrating the superiorperformance over three baseline classifiers when labeled training data islimited, and loads and topology are allowed to vary.

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