22 citations · 34 across the 39 of their papers we have counts for
5 papers · 1 filter
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 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…
Feature Selection for Fault Prediction in Distribution Systems
Georg Kordowich, Julian Oelhaf, Siming Bayer +3
While conventional power system protection isolates faulty components only after a fault has occurred, fault prediction approaches try to detect faults before they can cause signif…
Impact of Training Dataset Size for ML Load Flow Surrogates
Timon Conrad, Changhun Kim, Johann Jäger +2
Efficient and accurate load flow calculations are a bedrock of modern power system operation. Classical numerical methods such as the Newton-Raphson algorithm provide highly precis…