A method for identifying surface contamination components of composite insulators based on hyperspectral imaging
The surface contamination components of composite insulators significantly influence the pollution flashover voltage. To develop a more efficient and practical method for identifying typical contamination components， this paper proposes a hyperspectral imaging-based identification method. Firstly， s...
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| Format: | Article |
| Language: | zho |
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zhejiang electric power
2025-01-01
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| Series: | Zhejiang dianli |
| Subjects: | |
| Online Access: | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=7cbf33f1-28a0-46d6-8d5c-292ee94857fa |
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| _version_ | 1823857049983778816 |
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| author | MIAO Jin QIN Jun CHEN Junyu WU Junfeng REN Ming LIU Runyu |
| author_facet | MIAO Jin QIN Jun CHEN Junyu WU Junfeng REN Ming LIU Runyu |
| author_sort | MIAO Jin |
| collection | DOAJ |
| description | The surface contamination components of composite insulators significantly influence the pollution flashover voltage. To develop a more efficient and practical method for identifying typical contamination components， this paper proposes a hyperspectral imaging-based identification method. Firstly， silicone rubber samples with single or mixed contamination of seven typical pollutants are prepared using standard artificial contamination methods， and hyperspectral 2D pixel data of these samples are acquired under simulated sunlight conditions. Next， kernelized principal component analysis （KPCA） is introduced at the 2D pixel level to effectively extract pixel data features of the typical contamination components. A 3D feature subspace is then established in the spectral dimension using envelope elimination and KPCA. Finally， the kernel support vector machine （KSVM） algorithm is employed to identify the pixel features of the seven contamination components. Results show that the proposed method achieve an identification accuracy of 93.75% for contamination components on the silicone rubber surface and over 80% for mixed contaminants. This study provides a novel approach for rapid on-site analysis of contamination components and flashover characteristics of composite insulators， as well as a reference for live-line inspections and pollution degree measurements in laboratories. |
| format | Article |
| id | doaj-art-3a5a4f008c9b44efaddc05907b74bda0 |
| institution | Kabale University |
| issn | 1007-1881 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | zhejiang electric power |
| record_format | Article |
| series | Zhejiang dianli |
| spelling | doaj-art-3a5a4f008c9b44efaddc05907b74bda02025-02-12T00:54:59Zzhozhejiang electric powerZhejiang dianli1007-18812025-01-0144112413210.19585/j.zjdl.2025010131007-1881（2025）01-0124-09A method for identifying surface contamination components of composite insulators based on hyperspectral imagingMIAO Jin0QIN Jun1CHEN Junyu2WU Junfeng3REN Ming4LIU Runyu5State Grid Wuxi Power Supply Company， Wuxi， Jiangsu 214000， ChinaState Grid Wuxi Power Supply Company， Wuxi， Jiangsu 214000， ChinaState Grid Wuxi Power Supply Company， Wuxi， Jiangsu 214000， ChinaState Grid Wuxi Power Supply Company， Wuxi， Jiangsu 214000， ChinaState Key Laboratory of Electrical Insulation for Power Equipment， Xi’an Jiaotong University， Xi’an 710049， ChinaState Key Laboratory of Electrical Insulation for Power Equipment， Xi’an Jiaotong University， Xi’an 710049， ChinaThe surface contamination components of composite insulators significantly influence the pollution flashover voltage. To develop a more efficient and practical method for identifying typical contamination components， this paper proposes a hyperspectral imaging-based identification method. Firstly， silicone rubber samples with single or mixed contamination of seven typical pollutants are prepared using standard artificial contamination methods， and hyperspectral 2D pixel data of these samples are acquired under simulated sunlight conditions. Next， kernelized principal component analysis （KPCA） is introduced at the 2D pixel level to effectively extract pixel data features of the typical contamination components. A 3D feature subspace is then established in the spectral dimension using envelope elimination and KPCA. Finally， the kernel support vector machine （KSVM） algorithm is employed to identify the pixel features of the seven contamination components. Results show that the proposed method achieve an identification accuracy of 93.75% for contamination components on the silicone rubber surface and over 80% for mixed contaminants. This study provides a novel approach for rapid on-site analysis of contamination components and flashover characteristics of composite insulators， as well as a reference for live-line inspections and pollution degree measurements in laboratories.https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=7cbf33f1-28a0-46d6-8d5c-292ee94857fainsulating silicone rubberinsulation contaminationreflective spectral imagingcontamination degreespectral feature extraction |
| spellingShingle | MIAO Jin QIN Jun CHEN Junyu WU Junfeng REN Ming LIU Runyu A method for identifying surface contamination components of composite insulators based on hyperspectral imaging Zhejiang dianli insulating silicone rubber insulation contamination reflective spectral imaging contamination degree spectral feature extraction |
| title | A method for identifying surface contamination components of composite insulators based on hyperspectral imaging |
| title_full | A method for identifying surface contamination components of composite insulators based on hyperspectral imaging |
| title_fullStr | A method for identifying surface contamination components of composite insulators based on hyperspectral imaging |
| title_full_unstemmed | A method for identifying surface contamination components of composite insulators based on hyperspectral imaging |
| title_short | A method for identifying surface contamination components of composite insulators based on hyperspectral imaging |
| title_sort | method for identifying surface contamination components of composite insulators based on hyperspectral imaging |
| topic | insulating silicone rubber insulation contamination reflective spectral imaging contamination degree spectral feature extraction |
| url | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=7cbf33f1-28a0-46d6-8d5c-292ee94857fa |
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