# Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks

## TL;DR

This paper introduces a graph convolutional network framework for accurately locating faults in power distribution systems, effectively handling noise, data loss, and topology changes, outperforming existing machine learning methods.

## Contribution

The paper presents a novel GCN-based approach that integrates system topology and multiple measurements for fault location, demonstrating superior accuracy and robustness over existing methods.

## Key findings

- GCN significantly outperforms other machine learning schemes in fault location accuracy.
- The approach is robust to measurement noise and data loss.
- The model adapts well to topology changes and limited measurement data.

## Abstract

This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks. The proposed approach integrates multiple measurements at different buses while taking system topology into account. The effectiveness of the GCN model is corroborated by the IEEE 123 bus benchmark system. Simulation results show that the GCN model significantly outperforms other widely-used machine learning schemes with very high fault location accuracy. In addition, the proposed approach is robust to measurement noise and data loss errors. Data visualization results of two competing neural networks are presented to explore the mechanism of GCN's superior performance. A data augmentation procedure is proposed to increase the robustness of the model under various levels of noise and data loss errors. Further experiments show that the model can adapt to topology changes of distribution networks and perform well with a limited number of measured buses.