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
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
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
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