Author

Julian Oelhaf

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#artificial intelligence Preprint Oct 2026

Physics-Informed but Not Physics-Consistent: Error Geometry and Subspace Projection for Neural AC Power Flow

Recent neural power-flow solvers, including emerging foundation models, achieve accurate voltage predictions, yet such accuracy does not necessarily imply physically consistent solutions. Even small complex voltage errors can yield large AC power-balance residuals. We study this accuracy-consistency gap across PIGNN-GC...

Changhun Kim, Tim Conrad, Redwanul Karim et al. · 0 citations

#machine learning Preprint Sep 2026

Offline Reinforcement Learning for Distribution-Grid Protection

Data-driven protection may complement conventional relays in distribution grids whose operating conditions vary with distributed generation, switching events, and changing short-circuit levels. We study line-selective tripping from static trajectories of a realistically simulated CIGRE medium-voltage network using offl...

Julian Oelhaf, Alexander Luce, C. Bergler et al. · 0 citations

#machine learning Preprint Sep 2026

Peak-Aware Short-Term Load Forecasting Across Distribution Grid Aggregation Levels

For distribution system operators, short-term load forecasting (STLF) supports congestion management, voltage control, and asset protection. Most existing approaches focus on overall accuracy across all time steps and neglect performance during high-demand (HD) periods, where larger forecast errors can increase the ris...

Souhardya Chattopadhyay, Julian Oelhaf, A. Schoening et al. · 0 citations

#machine learning Review May 2025

Unsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals

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 lack of labeled datasets poses a significant challenge for fault classification and analysis. This paper explores the application of unsuper...

Julian Oelhaf, Georg Kordowich, Andreas K. Maier et al. · arXiv.org · 4 citations

Open access Aug 2026

A standardized framework for machine learning in power system protection

A standardization-oriented framework that turns evaluation assumptions into explicit, reproducible evidence and provides a basis for more comparable, auditable evaluation and future certification-oriented assessment of machine-learning protection functions is proposed.

Julian Oelhaf, Georg Kordowich, Paula Andrea Pérez-Toro et al. · International Journal of Ele... · 0 citations

Preprint Aug 2026

A simulation based dataset of faults and events for machine learning in power systems

The dataset is designed for training, fine-tuning, and benchmarking machine learning models by providing synchronized point-on-wave voltage and current measurements across a diverse set of topologies and voltage levels.

Georg Kordowich, Jonathan Loebel, Julian Oelhaf et al. · 2 citations · ⚡1