arXiv — Machine Learning · · 3 min read

PROTECT-90: A Fault Dataset for Power System Protection

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Electrical Engineering and Systems Science > Signal Processing

arXiv:2606.24298 (eess)
[Submitted on 23 Jun 2026]

Title:PROTECT-90: A Fault Dataset for Power System Protection

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Abstract: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 transparent and reproducible evaluation. To address this gap, this paper introduces the PROTECT-90 dataset, an open electromagnetic transient (EMT)-simulated reference benchmark for high-voltage fault studies with consistent digital-fault-recorder-like measurements, publicly released with this work. The dataset comprises 9,022 physically consistent short-circuit simulation episodes generated on a standardized 90 kV double-line topology with systematically documented domain randomization of grid operating points, line parameters, and fault conditions. For each episode, synchronized three-phase voltage and current waveforms are recorded at eight measurement locations and released together with structured, machine-readable metadata describing fault type, fault location, inception time, and operating conditions. All modeling assumptions, parameter ranges, and data-generation procedures are explicitly documented to ensure transparency and cross-study comparability. By combining physically grounded EMT simulation, balanced scenario coverage, and open accessibility, PROTECT-90 establishes a standardized foundation for reproducible benchmarking of protection-oriented signal processing and learning-based methods.
Comments: 6 pages, 3 figures, 3 tables. Accepted for publication at IEEE PES ISGT Europe 2026. Author accepted manuscript. Final published version will be available via IEEE Xplore
Subjects: Signal Processing (eess.SP); Machine Learning (cs.LG)
Cite as: arXiv:2606.24298 [eess.SP]
  (or arXiv:2606.24298v1 [eess.SP] for this version)
  https://doi.org/10.48550/arXiv.2606.24298
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Julian Oelhaf [view email]
[v1] Tue, 23 Jun 2026 08:25:25 UTC (192 KB)
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