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

本研究提出了一种异构多任务学习图神经网络（MTL-GNN），用于检测、定位和分类故障，同时提供故障电阻和电流的估计。使用图神经网络（GNN）学习配电系统的拓扑表示和特征学习，以及一种基于 GNN 的新颖可解释性方法以确定配电系统中的关键节点，从而实现知情稀疏测量。数值测试验证了该模型在所有任务上的性能。