A Method for Fault Localization in Distribution Networks with High Proportions of Distributed Generation Based on Graph Convolutional Networks

To address the issues arising from the integration of a high proportion of distributed generation (DG) into the distribution network, which has led to the transition from traditional single-source to multi-source distribution systems, resulting in increased complexity of the distribution network topology and difficulties in fault localization, this paper proposes a fault localization method based on graph convolutional networks (GCNs) for distribution networks with a high proportion of distributed generation. By abstracting busbars and lines into graph structure nodes and edges, GCN captures spatial coupling relationships between nodes, using key electrical quantities such as node voltage magnitude, current magnitude, power, and phase angle as input features to construct a fault localization model. A multi-type fault dataset is generated using the Matpower toolbox, and model training is evaluated using K-fold cross-validation. The training process is optimized through early stopping mechanisms and learning rate scheduling. Simulations are conducted based on the IEEE 33-node distribution network benchmark, with photovoltaic generation, wind generation, and energy storage systems connected at specific nodes, validating the model's fault localization capability under various fault types (single-phase ground fault, phase-to-phase short circuit, and line open circuit). Experimental results demonstrate that the proposed model can effectively locate fault nodes in complex distribution networks with high DG integration, achieving an accuracy of 98.5% and an AUC value of 0.9997. It still shows strong robustness in noisy environments and is significantly higher than convolutional neural networks and other methods in terms of model localization accuracy, training time, F1 score, AUC value, and single fault detection inference time, which has good potential for practical application.