Graph Neural Network with Graph-Conditioned Sequential Readout for Fault Localization in Power Grids

Fast bus-level fault localization is essential for
resilient power grids under renewable integration and changing
operating conditions. This work presents a Graph Neural Network with Graph-Conditioned Sequential Readout that combines
topology-aware message passing on the IEEE 39-bus system
with an LSTM readout over graph embeddings. Trained with
a multi-task objective for localization and auxiliary fault-type
prediction, the model is evaluated on a balanced synthetic dataset
of 60 544 samples. It achieves 99.8% fault-only top-1 localization,
100.0% top-3 localization, 100.0% fault detection, and 0.012 mean
absolute bus-index error. Random Forest saturates this synthetic
benchmark at 100.0%, and we identify the property of the data
generator responsible; the contribution is therefore the topologyaware formulation, the grouped evaluation protocol, and the
accompanying robustness analysis rather than a raw-accuracy
lead.