Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks
This addresses fault location for power distribution networks, offering a robust and accurate solution that is incremental in applying GCNs to this specific domain.
This paper tackled fault location in power distribution systems by developing a graph convolutional network (GCN) framework that integrates measurements and system topology, achieving very high accuracy and outperforming other machine learning schemes on the IEEE 123 bus benchmark system.
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.