46 citations · 74 across the 15 of their papers we have counts for
16 papers
EvEMTBench: An Open Benchmark for Machine Learning in Power System Protection
Julian Oelhaf, Georg Kordowich, Christian Bergler +3
Studies of machine-learning-based power system protection are difficult to compare because task definitions, measurement access, data partitions, metrics, and generalization condit…
Offline Reinforcement Learning for Distribution-Grid Protection
Julian Oelhaf, Alexander Luce, Christian Bergler +2
Data-driven protection may complement conventional relays in distribution grids whose operating conditions vary with distributed generation, switching events, and changing short-ci…
A Standardized Framework for Machine Learning in Power System Protection
Julian Oelhaf, Georg Kordowich, Paula Andrea Pérez-Toro +4
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. Prote…
A simulation based dataset of faults and events for machine learning in power systems
Georg Kordowich, Jonathan Loebel, Julian Oelhaf +4
The integration of inverter-based renewable energy sources into electric grids challenges conventional power system protection. Machine learning-based solutions can address these c…
DSCH-Loss: A Dynamic Semantic Channel Objective for Deep Semantic Hashing
Tobias J. Bauer, Christian Riess, Daniel Loebenberger +1
Semantic hashing methods for generating short binary hash codes that allow efficient approximate nearest neighbor search in high-dimensional data spaces have gained extensive consi…
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…