Generalization of Graph Neural Network Models for Distribution Grid Fault Detection

• URL: http://arxiv.org/abs/2510.03571v1
• Date: Fri, 03 Oct 2025 23:46:31 GMT
• Title: Generalization of Graph Neural Network Models for Distribution Grid Fault Detection
• Authors: Burak Karabulut, Carlo Manna, Chris Develder,
• Abstract summary: This paper systematically benchmarks various Graph Neural Networks (GNNs) in an RNN+GNN pipeline model.<n>To the best of our knowledge, we are the first to (i) propose to use GraphSAGE and Graph Attention (GAT, GATv2) in an RGNN for fault diagnosis.<n>Our experimental results on the IEEE 123-node distribution network show that RGATv2 has superior generalization capabilities.
• Score: 2.183622338864934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fault detection in power distribution grids is critical for ensuring system reliability and preventing costly outages. Moreover, fault detection methodologies should remain robust to evolving grid topologies caused by factors such as reconfigurations, equipment failures, and Distributed Energy Resource (DER) integration. Current data-driven state-of-the-art methods use Recurrent Neural Networks (RNNs) for temporal modeling and Graph Neural Networks (GNNs) for spatial learning, in an RNN+GNN pipeline setting (RGNN in short). Specifically, for power system fault diagnosis, Graph Convolutional Networks (GCNs) have been adopted. Yet, various more advanced GNN architectures have been proposed and adopted in domains outside of power systems. In this paper, we set out to systematically and consistently benchmark various GNN architectures in an RNN+GNN pipeline model. Specifically, to the best of our knowledge, we are the first to (i) propose to use GraphSAGE and Graph Attention (GAT, GATv2) in an RGNN for fault diagnosis, and (ii) provide a comprehensive benchmark against earlier proposed RGNN solutions (RGCN) as well as pure RNN models (especially Gated Recurrent Unit (GRU)), particularly (iii) exploring their generalization potential for deployment in different settings than those used for training them. Our experimental results on the IEEE 123-node distribution network show that RGATv2 has superior generalization capabilities, maintaining high performance with an F1-score reduction of $\sim$12% across different topology settings. In contrast, pure RNN models largely fail, experiencing an F1-score reduction of up to $\sim$60%, while other RGNN variants also exhibit significant performance degradation, i.e., up to $\sim$25% lower F1-scores.

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