6 papers
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…
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…
Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection
Julian Oelhaf, Georg Kordowich, Changhun Kim +5
The increasing complexity of modern power systems, driven by the integration of inverter-based and distributed energy resources, challenges the reliability of conventional protecti…
Robustness Evaluation of Machine Learning Models for Fault Classification and Localization In Power System Protection
Julian Oelhaf, Mehran Pashaei, Georg Kordowich +4
The growing penetration of renewable and distributed generation is transforming power systems and challenging conventional protection schemes that rely on fixed settings and local…
A Scoping Review of Machine Learning Applications in Power System Protection and Disturbance Management
Julian Oelhaf, Georg Kordowich, Mehran Pashaei +4
The integration of renewable and distributed energy resources reshapes modern power systems, challenging conventional protection schemes. This scoping review synthesizes recent lit…