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<p>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.</p>

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Terabyte scale dataset for partial discharge detection in covered conductors via contact galvanic method

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月28日

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  • Michal Krátký,
  • Jan Fulneček,
  • Lukáš Klein,
  • Radim Bača,
  • Peter Chovanec,
  • Petr Lukáš,
  • Prokop Lukáš,
  • Ondřej Kabot,
  • Jiří Dvorský,
  • Stanislav Mišák

摘要

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.