mlsanityData to 14 Oct 2026

PROTECT-90: A Fault Dataset for Power System Protection

arXiv 2606.24298 ↗First posted 23 Jun 2026eess.SP cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 4 of its 16 references that are in the map. First posted on arXiv 23 Jun 2026.

Builds on

4 of 16 references.

References · 4

  1. 2025A Scoping Review of Machine Learning Applications in Power System Protection and Disturbance Managementcited by 9
  2. 2025Impact of Data Sparsity on Machine Learning for Fault Detection in Power System Protectioncited by 6
  3. 2025Robustness Evaluation of Machine Learning Models for Fault Classification and Localization In Power System Protectioncited by 5
  4. 2025Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protectioncited by 4

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