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arXiv Machine Learning By Nithin Pradeep Nadayil Kizhakkethil, Julian Oelhaf, Christian Bergler, Andreas Maier, Siming Bayer

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

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The study examines how machine‑learning fault detectors trained on data from specific relay locations perform when applied to other relays in a 110 kV transmission network. Using 50 ms windows and a multilayer perceptron, the authors evaluate all 64 source–target relay combinations and find that overall F1‑scores remain similar, but the false‑positive rate rises markedly while the false‑negative rate falls when models are transferred across relays. Analysis shows that models retain coarse bus‑group information, and that aggregate metrics can mask location‑specific changes in false‑alarm behavior, underscoring the need to assess error types separately for cross‑relay generalization.

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