  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.
