Characterizing Cross-Relay Generalization Failure in Machine Learning-Based Power System Protection

Machine learning-based fault detectors are often evaluated at the relay locations used for training, although local voltage and current measurements vary across power networks. This work investigates cross-relay generalization for binary detection of faults anywhere in the network using local measurements, rather than line-selective tripping. Using 50 ms windows from a simulated 110 kV transmission network, we evaluate a multilayer perceptron across all 64 source target combinations of eight relays and five random seeds. A shared simulation-level split and source-only threshold selection provide a controlled held-out evaluation without target-specific calibration. Despite nearly unchanged mean F1-scores (0.730 within-relay versus 0.734 cross-relay), the mean false-positive rate (FPR) increases from 0.088 to 0.341, while the mean false-negative rate decreases from 0.399 to 0.318. The change is strongly directional: models trained at Bus 2 relays yield a mean FPR of 0.670 at Bus 1 and Bus 3 relays, compared with 0.096 for cross-relay transfers within the same bus group. Linear probing indicates that learned representations retain coarse bus-group information, while exploratory class-conditional latent distribution analysis shows no consistent monotonic association with transfer errors. These findings demonstrate that aggregate metrics can conceal location-structured changes in false-alarm behavior, highlighting the importance of evaluating error types separately when assessing cross-relay generalization.