PPGN: Physics-Preserved Graph Networks for Real-Time Fault Location in Distribution Systems with Limited Observation and Labels. (arXiv:2107.02275v3 [cs.LG] UPDATED)

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 …

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