Fault Inception Detection in Real-World Disturbance Data for Power System Protection

Oelhaf J, Pashaei M, Perez-Toro PA, Kordowich G, Bergler C, Maier A, Jäger J, Bayer S (2026)

Publication Status: Submitted

Publication Type: Unpublished / Preprint

Future Publication Type: Journal article

Publication year: 2026

Original Authors: Julian Oelhaf, Mehran Pashaei, Paula Andrea Perez-Toro, Georg Kordowich, Christian Bergler, Andreas Maier, Johann Jaeger, Siming Bayer

DOI: 10.48550/arXiv.2606.23111

Abstract

Large collections of real-world disturbance recordings are increasingly available in transmission networks, but their value for power system protection and automated disturbance analysis is limited by the absence of precise event-onset annotations. In practice, field-recorded voltage and current waveforms contain switching operations, transformer energization, resonance, saturation, and other non-ideal effects that can obscure or mimic genuine fault signatures, making reliable fault inception detection difficult. This paper presents an training-free framework for fault inception detection in real-world transmission disturbance data. The method combines protection-domain indicators, robust median/MAD-based normalization, a low-latency transient path, and persistence-aware fusion and veto logic to distinguish fault-consistent disturbances from non-fault transients. We apply the framework to 12053 transmission-level recordings from the publicly available RTE database and further assess detector performance on a manually reviewed subset of 300 events. On the reviewed subset, the detector achieves 96.6% recall, 79.2% precision, and a median timing error of 4.2ms for matched detections. These results indicate that the proposed approach can support protection-oriented disturbance screening, relay and post-event analysis, and the creation of timestamp annotations for downstream data-driven monitoring tasks.

Authors with CRIS profile

Involved external institutions

Universidad de Antioquía (UDEA) Colombia (CO)

How to cite

APA:

Oelhaf, J., Pashaei, M., Perez-Toro, P.A., Kordowich, G., Bergler, C., Maier, A.,... Bayer, S. (2026). Fault Inception Detection in Real-World Disturbance Data for Power System Protection. (Unpublished, Submitted).

MLA:

Oelhaf, Julian, et al. Fault Inception Detection in Real-World Disturbance Data for Power System Protection. Unpublished, Submitted. 2026.

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