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

Key points

• This work investigates cross-relay generalization for binary detection of faults anywhere in the network using local measurements, rather than line-selective tripping.1
• 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.1
• 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.1
• 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.1

Sources (1)

• [1]Characterizing Cross-Relay Generalization Failure in Machine Learning-Based Power System Protection
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 03:13 PM

  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.

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