Feature extraction of partial discharge in low‐temperature composite insulation based on VMD‐MSE‐IF
Abstract Low‐temperature composite insulation is commonly applied in high‐temperature superconducting apparatus while partial discharge (PD) is found to be an important indicator to reveal insulation statues. In order to extract feature parameters of PD signals more effectively, a method combined va...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-06-01
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| Series: | CAAI Transactions on Intelligence Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cit2.12087 |
| _version_ | 1811188514994257920 |
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| author | Xi Chen Xiao Shao Xin Pan Gaochao Luo Maoqiang Bi Tianyan Jiang Kang Wei |
| author_facet | Xi Chen Xiao Shao Xin Pan Gaochao Luo Maoqiang Bi Tianyan Jiang Kang Wei |
| author_sort | Xi Chen |
| collection | DOAJ |
| description | Abstract Low‐temperature composite insulation is commonly applied in high‐temperature superconducting apparatus while partial discharge (PD) is found to be an important indicator to reveal insulation statues. In order to extract feature parameters of PD signals more effectively, a method combined variational mode decomposition with multi‐scale entropy and image feature is proposed. Based on the simulated test platform, original and noisy signals of three typical PD defects were obtained and decomposed. Accordingly, relative moments and grayscale co‐occurrence matrix were employed for feature extraction by K‐modal component diagram. Afterwards, new PD feature vectors were obtained by dimension reduction. Finally, effectiveness of different feature extraction methods was evaluated by pattern recognition based on support vector machine and K‐nearest neighbour. Result shows that the proposed feature extraction method has a higher recognition rate by comparison and is robust in processing noisy signals. |
| first_indexed | 2024-04-11T14:19:58Z |
| format | Article |
| id | doaj.art-653f66ffb67a412f9d9cc9ba34b79696 |
| institution | Directory Open Access Journal |
| issn | 2468-2322 |
| language | English |
| last_indexed | 2024-04-11T14:19:58Z |
| publishDate | 2022-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj.art-653f66ffb67a412f9d9cc9ba34b796962022-12-22T04:19:05ZengWileyCAAI Transactions on Intelligence Technology2468-23222022-06-017230131210.1049/cit2.12087Feature extraction of partial discharge in low‐temperature composite insulation based on VMD‐MSE‐IFXi Chen0Xiao Shao1Xin Pan2Gaochao Luo3Maoqiang Bi4Tianyan Jiang5Kang Wei6School of Electrical and Electronic Engineering Chongqing University of Technology Chongqing ChinaSchool of Electrical and Electronic Engineering Chongqing University of Technology Chongqing ChinaSchool of Electrical and Electronic Engineering Chongqing University of Technology Chongqing ChinaChongqing Wanzhou District Municipal Facilities Maintenance and Management Center Chongqing ChinaSchool of Electrical and Electronic Engineering Chongqing University of Technology Chongqing ChinaSchool of Electrical and Electronic Engineering Chongqing University of Technology Chongqing ChinaMeta Platforms Inc Menlo Park California USAAbstract Low‐temperature composite insulation is commonly applied in high‐temperature superconducting apparatus while partial discharge (PD) is found to be an important indicator to reveal insulation statues. In order to extract feature parameters of PD signals more effectively, a method combined variational mode decomposition with multi‐scale entropy and image feature is proposed. Based on the simulated test platform, original and noisy signals of three typical PD defects were obtained and decomposed. Accordingly, relative moments and grayscale co‐occurrence matrix were employed for feature extraction by K‐modal component diagram. Afterwards, new PD feature vectors were obtained by dimension reduction. Finally, effectiveness of different feature extraction methods was evaluated by pattern recognition based on support vector machine and K‐nearest neighbour. Result shows that the proposed feature extraction method has a higher recognition rate by comparison and is robust in processing noisy signals.https://doi.org/10.1049/cit2.12087feature extractionpattern recognition |
| spellingShingle | Xi Chen Xiao Shao Xin Pan Gaochao Luo Maoqiang Bi Tianyan Jiang Kang Wei Feature extraction of partial discharge in low‐temperature composite insulation based on VMD‐MSE‐IF CAAI Transactions on Intelligence Technology feature extraction pattern recognition |
| title | Feature extraction of partial discharge in low‐temperature composite insulation based on VMD‐MSE‐IF |
| title_full | Feature extraction of partial discharge in low‐temperature composite insulation based on VMD‐MSE‐IF |
| title_fullStr | Feature extraction of partial discharge in low‐temperature composite insulation based on VMD‐MSE‐IF |
| title_full_unstemmed | Feature extraction of partial discharge in low‐temperature composite insulation based on VMD‐MSE‐IF |
| title_short | Feature extraction of partial discharge in low‐temperature composite insulation based on VMD‐MSE‐IF |
| title_sort | feature extraction of partial discharge in low temperature composite insulation based on vmd mse if |
| topic | feature extraction pattern recognition |
| url | https://doi.org/10.1049/cit2.12087 |
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