Robustness of Spatio-temporal Graph Neural Networks for Fault Location in Partially Observable Distribution Grids
Fault location in distribution grids is critical for reliability and minimizing outage durations. Yet, it remains challenging due to partial observability, given sparse measurement infrastructure. Recent works show promising results by combining Recurrent Neural Networks (RNNs) and Graph Neural Networks (GNNs) for spatio-temporal learning. Still, many modern GNN architectures remain untested for this grid application, while existing GNN solutions have not explored GNN topology definitions beyond simply adopting the full grid topology to construct the GNN graph. We address these gaps by (i) systematically comparing a newly proposed graph-forming strategy (measured-only) to the traditional ...
| Authors | Burak Karabulut, Carlo Manna, Chris Develder |
|---|---|
| Primary category | cs.LG |
| Published | 22 April 2026 |
| Last updated at source | 8 May 2026 |
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