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...
| Authors | Nithin Pradeep Nadayil Kizhakkethil, Julian Oelhaf, Christian Bergler et al. |
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| Primary category | cs.LG |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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