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
Multimodal medical prediction often faces incomplete pairing: auxiliary modalities with complementary signal are available for only a subset of subjects (or none) and cannot be assumed at deployment. We introduce PANDA (Prototype Anchored Data Alignment), a two-stage framework that transfers auxiliary information to a...
Sheethal Bhat, Mahfuzur Rahman Chowdhury, P. A. Pérez-Toro et al.· 0 citations
With this ensemble, frequency matching with a public name list recovers zero identities by alignment across the four corpora; the names it got right were ones the detector missed, left in clear text.
Andreas K. Maier, Monica Hinrichs-Mayer, Franziska Weber et al.· 0 citations
EvEMTBench makes generalization in machine-learning-based protection an explicit and reproducible evaluation problem and addresses an open, executable, and versioned benchmark that fixes these evaluation choices while leaving model design open.
Julian Oelhaf, Georg Kordowich, C. Bergler et al.· 0 citations
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
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
This study assesses the efficacy of various ML models in detecting electrical faults and pinpointing defective transmission lines within a 10 ms measurement interval—a critical time-frame for real-time operational viability, for the first time.
Julian Oelhaf, Georg Kordowich, P. A. Pérez-Toro et al.· IEEE International Conferenc...· 12 citations
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
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
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
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