most citedA Scoping Review of Machine Learning Applications in Power System Protection and Disturbance Management

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eess.SP2026

PROTECT-90: A Fault Dataset for Power System Protection

Julian Oelhaf, Georg Kordowich, Christian Bergler +3

The increasing interest in data-driven methods for power system protection is accompanied by a lack of standardized, publicly available high-voltage waveform datasets that enable t…

eess.SP2026

Fault Inception Detection in Real-World Disturbance Data for Power System Protection

Julian Oelhaf, Mehran Pashaei, Paula Andrea Perez-Toro +5

Large collections of real-world disturbance recordings are increasingly available in transmission networks, but their value for power system protection and automated disturbance an…