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.

翻译：精确且及时的故障诊断是配电系统实现最小停机时间并维持可靠运行的前提条件。这要求具备一套全面流程，能在发生故障事件时为电网运营商提供洞察性信息。本文提出了一种异构多任务学习图神经网络（MTL-GNN），该网络能够检测、定位和分类故障，同时提供故障电阻和电流的估计值。采用图神经网络（GNN）可通过消息传递机制学习配电系统的拓扑表示及特征学习。我们基于IEEE-123测试馈线系统验证了所提模型的鲁棒性。本研究还提出了一种新颖的基于GNN的可解释性方法，用于识别配电系统中的关键节点，从而促进稀疏测量的合理化配置。数值测试验证了该模型在所有任务上的性能表现。