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Precise and timely fault diagnosis is a prerequisite for a distributionsystem to ensure minimum downtime and maintain reliable operation. Thisnecessitates access to a comprehensive procedure that can provide the gridoperators with insightful information in the case of a fault event. In thispaper, we propose a heterogeneous multi-task learning graph neural network(MTL-GNN) capable of detecting, locating and classifying faults in addition toproviding an estimate of the fault resistance and current. Using a graph neuralnetwork (GNN) allows for learning the topological representation of thedistribution system as well as feature learning through a message-passingscheme. We investigate the robustness of our proposed model using the IEEE-123test feeder system. This work also proposes a novel GNN-based explainabilitymethod to identify key nodes in the distribution system which then facilitatesinformed sparse measurements. Numerical tests validate the performance of themodel across all tasks.

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