Power-Line Partial Discharge Recognition with Hilbert–Huang Transform Features
Partial discharge (PD) has caused considerable challenges to the safety and stability of high voltage equipment. Therefore, highly accurate and effective PD detection has become the focus of research. Hilbert–Huang Transform (HHT) features have been proven to have great potential in the PD analysis...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/18/6521 |
| _version_ | 1797488978795954176 |
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| author | Yulu Wang Hsiao-dong Chiang Na Dong |
| author_facet | Yulu Wang Hsiao-dong Chiang Na Dong |
| author_sort | Yulu Wang |
| collection | DOAJ |
| description | Partial discharge (PD) has caused considerable challenges to the safety and stability of high voltage equipment. Therefore, highly accurate and effective PD detection has become the focus of research. Hilbert–Huang Transform (HHT) features have been proven to have great potential in the PD analysis of transformer, gas insulated switchgear and power cable. However, due to the insufficient research available on the PD features of power lines, its application in the PD recognition of power lines has not yet been systematically studied. In the present study, an enhanced light gradient boosting machine methodology for PD recognition is proposed; the HHT features are extracted from the signal and added to the feature pool to improve the performance of the classifier. A public power-line PD recognition contest dataset is introduced to evaluate the effectiveness of the proposed feature. Numerical studies along with comparison results demonstrate that the proposed method can achieve promising performances. This method which includes the HHT features contributes to the detection of PD in power lines. |
| first_indexed | 2024-03-10T00:09:58Z |
| format | Article |
| id | doaj.art-42c23798aa6543049ffcc343afc3737c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T00:09:58Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-42c23798aa6543049ffcc343afc3737c2023-11-23T16:01:08ZengMDPI AGEnergies1996-10732022-09-011518652110.3390/en15186521Power-Line Partial Discharge Recognition with Hilbert–Huang Transform FeaturesYulu Wang0Hsiao-dong Chiang1Na Dong2School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaPartial discharge (PD) has caused considerable challenges to the safety and stability of high voltage equipment. Therefore, highly accurate and effective PD detection has become the focus of research. Hilbert–Huang Transform (HHT) features have been proven to have great potential in the PD analysis of transformer, gas insulated switchgear and power cable. However, due to the insufficient research available on the PD features of power lines, its application in the PD recognition of power lines has not yet been systematically studied. In the present study, an enhanced light gradient boosting machine methodology for PD recognition is proposed; the HHT features are extracted from the signal and added to the feature pool to improve the performance of the classifier. A public power-line PD recognition contest dataset is introduced to evaluate the effectiveness of the proposed feature. Numerical studies along with comparison results demonstrate that the proposed method can achieve promising performances. This method which includes the HHT features contributes to the detection of PD in power lines.https://www.mdpi.com/1996-1073/15/18/6521partial dischargeHilbert–Huang TransformLightGBMfeature extraction |
| spellingShingle | Yulu Wang Hsiao-dong Chiang Na Dong Power-Line Partial Discharge Recognition with Hilbert–Huang Transform Features Energies partial discharge Hilbert–Huang Transform LightGBM feature extraction |
| title | Power-Line Partial Discharge Recognition with Hilbert–Huang Transform Features |
| title_full | Power-Line Partial Discharge Recognition with Hilbert–Huang Transform Features |
| title_fullStr | Power-Line Partial Discharge Recognition with Hilbert–Huang Transform Features |
| title_full_unstemmed | Power-Line Partial Discharge Recognition with Hilbert–Huang Transform Features |
| title_short | Power-Line Partial Discharge Recognition with Hilbert–Huang Transform Features |
| title_sort | power line partial discharge recognition with hilbert huang transform features |
| topic | partial discharge Hilbert–Huang Transform LightGBM feature extraction |
| url | https://www.mdpi.com/1996-1073/15/18/6521 |
| work_keys_str_mv | AT yuluwang powerlinepartialdischargerecognitionwithhilberthuangtransformfeatures AT hsiaodongchiang powerlinepartialdischargerecognitionwithhilberthuangtransformfeatures AT nadong powerlinepartialdischargerecognitionwithhilberthuangtransformfeatures |