PROTECT-90: A Fault Dataset for Power System Protection
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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
- 2025A Scoping Review of Machine Learning Applications in Power System Protection and Disturbance Managementcited by 9
- 2025Impact of Data Sparsity on Machine Learning for Fault Detection in Power System Protectioncited by 6
- 2025Robustness Evaluation of Machine Learning Models for Fault Classification and Localization In Power System Protectioncited by 5
- 2025Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protectioncited by 4
Built on by
Citing papers per year: 2026: 4.
4 papers.
- 2026A Standardized Framework for Machine Learning in Power System Protectioncited by 1
- 2026Discovering Physical Representation Languagescited by 0
- 2026EvEMTBench: An Open Benchmark for Machine Learning in Power System Protectioncited by 0
- 2026One-Cycle Fault Classification and Faulted-Line Identification on the PROTECT-90 Dataset: An Initial Application Benchmarkcited by 0
- 2026One-Cycle Fault Classification and Faulted-Line Identification on the PROTECT-90 Dataset: An Initial Application Benchmark2 Oct 2026
- 2026EvEMTBench: An Open Benchmark for Machine Learning in Power System Protection23 Sep 2026
- 2026Discovering Physical Representation Languages20 Sep 2026
- 2026A Standardized Framework for Machine Learning in Power System Protection20 Aug 2026