22 citations · 25 across the 13 of their papers we have counts for
13 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…
A Systematic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids
Julian Oelhaf, Georg Kordowich, Paula Andrea Pérez-Toro +4
The integration of renewable energy sources into the electrical grid introduces complex challenges in fault detection and coordination of grid recovery mechanisms. Traditional rela…
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…
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…
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…
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…
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…
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…
Unsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals
Julian Oelhaf, Georg Kordowich, Andreas Maier +3
The widespread use of sensors in modern power grids has led to the accumulation of large amounts of voltage and current waveform data, especially during fault events. However, the…
Impact of Data Sparsity on Machine Learning for Fault Detection in Power System Protection
Julian Oelhaf, Georg Kordowich, Changhun Kim +4
Germany's transition to a renewable energy-based power system is reshaping grid operations, requiring advanced monitoring and control to manage decentralized generation. Machine le…
A Physics Informed Machine Learning Method for Power System Model Parameter Optimization
Georg Kordowich, Johann Jaeger
This paper proposes a gradient descent based optimization method that relies on automatic differentiation for the computation of gradients. The method uses tools and techniques ori…