Partial vs. Corona Discharges in XLPE-Covered Conductors: High-Resolution Antenna Dataset for ML Applications
Abstract Accurate differentiation between partial discharges (PD) and corona discharges in XLPE-covered conductors is crucial for power system diagnostics, yet remains limited by the lack of specialized, high-fidelity datasets for machine learning (ML) model development. This paper presents a high-r...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05627-z |
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| _version_ | 1849388326521405440 |
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| author | Ondřej Kabot Lukáš Klein Zdeněk Slanina Lukáš Prokop |
| author_facet | Ondřej Kabot Lukáš Klein Zdeněk Slanina Lukáš Prokop |
| author_sort | Ondřej Kabot |
| collection | DOAJ |
| description | Abstract Accurate differentiation between partial discharges (PD) and corona discharges in XLPE-covered conductors is crucial for power system diagnostics, yet remains limited by the lack of specialized, high-fidelity datasets for machine learning (ML) model development. This paper presents a high-resolution dataset (107 samples per 20 ms) acquired using a contactless dual-antenna system under controlled laboratory conditions simulating medium-voltage overhead distribution lines. The dataset includes 100 labeled measurements per class across five discharge types (PD, corona, mixed states, and high-impedance variants) and two background conditions (with and without high voltage), collected over a two-day campaign. By providing experimentally isolated signal types, this resource enables the development and benchmarking of ML models specifically tailored to the PD–corona classification challenge. Key applications include lightweight classification models for edge devices, synthetic data generation to augment limited training sets, and investigations into noise robustness, real-time monitoring, and explainable diagnostics. Through a controlled yet realistic acquisition design, the dataset supports the creation of advanced ML-based tools for non-invasive fault identification—enhancing diagnostic accuracy, mitigating insulation risks, and improving safety in critical power infrastructure. |
| format | Article |
| id | doaj-art-fa823e95417c44428b64bdd4b53a6acb |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-fa823e95417c44428b64bdd4b53a6acb2025-08-20T03:42:19ZengNature PortfolioScientific Data2052-44632025-08-0112111910.1038/s41597-025-05627-zPartial vs. Corona Discharges in XLPE-Covered Conductors: High-Resolution Antenna Dataset for ML ApplicationsOndřej Kabot0Lukáš Klein1Zdeněk Slanina2Lukáš Prokop3ENET centre - CEET, VSB - Technical University of OstravaENET centre - CEET, VSB - Technical University of OstravaENET centre - CEET, VSB - Technical University of OstravaENET centre - CEET, VSB - Technical University of OstravaAbstract Accurate differentiation between partial discharges (PD) and corona discharges in XLPE-covered conductors is crucial for power system diagnostics, yet remains limited by the lack of specialized, high-fidelity datasets for machine learning (ML) model development. This paper presents a high-resolution dataset (107 samples per 20 ms) acquired using a contactless dual-antenna system under controlled laboratory conditions simulating medium-voltage overhead distribution lines. The dataset includes 100 labeled measurements per class across five discharge types (PD, corona, mixed states, and high-impedance variants) and two background conditions (with and without high voltage), collected over a two-day campaign. By providing experimentally isolated signal types, this resource enables the development and benchmarking of ML models specifically tailored to the PD–corona classification challenge. Key applications include lightweight classification models for edge devices, synthetic data generation to augment limited training sets, and investigations into noise robustness, real-time monitoring, and explainable diagnostics. Through a controlled yet realistic acquisition design, the dataset supports the creation of advanced ML-based tools for non-invasive fault identification—enhancing diagnostic accuracy, mitigating insulation risks, and improving safety in critical power infrastructure.https://doi.org/10.1038/s41597-025-05627-z |
| spellingShingle | Ondřej Kabot Lukáš Klein Zdeněk Slanina Lukáš Prokop Partial vs. Corona Discharges in XLPE-Covered Conductors: High-Resolution Antenna Dataset for ML Applications Scientific Data |
| title | Partial vs. Corona Discharges in XLPE-Covered Conductors: High-Resolution Antenna Dataset for ML Applications |
| title_full | Partial vs. Corona Discharges in XLPE-Covered Conductors: High-Resolution Antenna Dataset for ML Applications |
| title_fullStr | Partial vs. Corona Discharges in XLPE-Covered Conductors: High-Resolution Antenna Dataset for ML Applications |
| title_full_unstemmed | Partial vs. Corona Discharges in XLPE-Covered Conductors: High-Resolution Antenna Dataset for ML Applications |
| title_short | Partial vs. Corona Discharges in XLPE-Covered Conductors: High-Resolution Antenna Dataset for ML Applications |
| title_sort | partial vs corona discharges in xlpe covered conductors high resolution antenna dataset for ml applications |
| url | https://doi.org/10.1038/s41597-025-05627-z |
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