ArticleScientific data2026
Terabyte scale dataset for partial discharge detection in covered conductors via contact galvanic method.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Terabyte scale dataset for partial discharge detection in covered conductors via contact galvanic method.Scientific data · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article introduces a dataset designed for the detection of partial discharges in transmission power lines using covered conductors through a contact galvanic method, sourced from real environments across 23 different power lines in various locations. Though partially introduced in a Kaggle competition (only 3% of data), its full extent is disclosed here for the first time. The dataset is distinguished by its rich, imbalanced distribution across seven classes, derived from signals processed via a sophisticated voltage-based method, and supplemented with extracted features to aid analysis. Its scale, detailed labeling, and real-world basis offer unparalleled opportunities for developing machine learning algorithms aimed at fault detection. This contribution holds vast potential for reuse in electrical engineering research focused on enhancing power distribution network reliability and safety, particularly in the context of predictive maintenance and understanding partial discharge behaviors.
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Registered trials
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