Integrated Fault Location Method Using Feeder-Centric Graph Neural Networks for Renewable-Penetrated DC Distribution Networks

Abstract

This paper proposes a fault location method for DC distribution networks (DCDNs) based on graph neural networks (GNNs), which integrates the fault line selection (FLS) and fault distance estimation (FDE) that are conventionally handled independently. The proposed method focuses on the analysis of feeders, including FLS of multiple feeders and FDE of a single feeder. Specifically, a feeder-as-node graph is constructed, where synchronous measurement data are extracted as node features, ensuring consistent dimensionality and enhanced learning efficiency. Moreover, the proposed method explicitly embeds the logical interdependencies between FLS and FDE into the structural design and parameter updating mechanism. An output processing module is designed to estimate the fault distance by analyzing the FLS results, ensuring the model utilizes the data of the DCDN system-level information rather than a single feeder. Furthermore, a two-stage pre-training strategy is introduced to improve stability and generalization, in which partial parameters are frozen. The effectiveness and generalization of the proposed method are verified by hardware-in-the-loop experiments.