A Standardized Framework for Machine Learning in Power System Protection
[Uczenie maszynowe] Autorzy: Julian Oelhaf, Georg Kordowich, Paula Andrea Pérez-Toro i 4 więcej
Studies of machine-learning-based power-system protection increasingly report near-perfect scores, yet the meaning of those scores depends strongly on the evaluation setting. Protection task, physical scope, measurements, timing, targets, preprocessing, and validation often vary jointly and remain incompletely specified. This paper proposes a standardization-oriented framework that treats evaluation design as part of the scientific contribution. It defines seven required study dimensions: protection objective, physical scope, observability, timing and decision windows, targets and sample validity, validation protocol, and evaluation outputs. The framework is instantiated in a bounded case study on the public PROTECT-90 electromagnetic-transient benchmark, comprising 9022 simulated episodes from a 90 kV double-line topology, for onset-conditioned fault classification and localization. Un…
- Kategoria arxiv
- cs.LG
- Kategoria
- Uczenie maszynowe