Fault localization is a key task on power systems operation and maintenance. When it comes to distribution networks, the problem is especially challenging due to the non-homogeneous characteristics and unique topology of each feeder. This paper presents a method based on gated graph neural network for automatic fault localization on distribution networks. The method aggregates problem data in a graph, where the feeder topology is represented by the graph links and nodes attributes can encapsulate any selected information such as operated devices, electrical characteristics and measurements at the point. The main advantage of the proposed solution is that it is immune to network reconfiguration and allows the use of a single trained model on multiple feeders. An experiment was conducted with faults simulated on 10 different feeders, all of them based on actual distribution feeders. The results shows that the model is able to generalize the correlations learned on training to correctly predict the fault region in most cases, even on a feeder it has not seen before.

故障定位是电力系统运行和维护的关键任务。当涉及配电网时，由于每个馈线的特性不均一且拓扑独特，这个问题尤其具有挑战性。提出了一种基于门控图神经网络的配电网故障自动定位方法。该方法将问题数据汇总到一个图形中，其中馈线拓扑由图形链接表示，节点属性可以封装任何选定的信息，例如该点处的操作设备，电气特性和测量值。提出的解决方案的主要优点是它不受网络重新配置的影响，并允许在多个馈线上使用单个训练模型。进行了一个实验，在10个不同的馈线上模拟了故障，所有这些都是基于实际的分配器。结果表明，该模型能够在大多数情况下，即使在之前从未见过的馈线上，也可以概括在训练中学到的相关性，以正确预测故障区域。