A GIS Partial Discharge Pattern Recognition Method Based on Improved CBAM-ResNet
Different types of partial discharge (PD) cause different damages to gas-insulated substation (GIS), so it is very important to correctly identify the type of PD for evaluating the GIS insulation condition. The traditional PD pattern recognition algorithm has the limitations of low recognition accur...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/9948438 |
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| _version_ | 1850166982934003712 |
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| author | Di Hu Zhong Chen Wei Yang Taiyun Zhu Yanguo Ke Kaiyang Yin |
| author_facet | Di Hu Zhong Chen Wei Yang Taiyun Zhu Yanguo Ke Kaiyang Yin |
| author_sort | Di Hu |
| collection | DOAJ |
| description | Different types of partial discharge (PD) cause different damages to gas-insulated substation (GIS), so it is very important to correctly identify the type of PD for evaluating the GIS insulation condition. The traditional PD pattern recognition algorithm has the limitations of low recognition accuracy and slow recognition speed in engineering applications. To effectively diagnose the GIS PD type and safeguard the safe and reliable operation of the distribution network, a GIS PD method based on improved CBAM-ResNet was proposed in this paper. And the improved CBAM-ResNet takes advantage of the residual neural network and attention mechanism. In particular, the channel attention module and the spatial attention module are connected in parallel in the improved CBAM. The experimental results showed that the GIS PD pattern recognition method proposed herein has a recognition rate of 93.58%, 95.00%, 93.55%, and 93.88% against the four PD types. Compared with the traditional PD pattern recognition algorithm, the algorithm has the advantages of a lightweight model and more accurate recognition results, which carry better engineering application values. |
| format | Article |
| id | doaj-art-e5118a10923c4d178f6644a81b93bdde |
| institution | OA Journals |
| issn | 2090-0155 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-e5118a10923c4d178f6644a81b93bdde2025-08-20T02:21:18ZengWileyJournal of Electrical and Computer Engineering2090-01552023-01-01202310.1155/2023/9948438A GIS Partial Discharge Pattern Recognition Method Based on Improved CBAM-ResNetDi Hu0Zhong Chen1Wei Yang2Taiyun Zhu3Yanguo Ke4Kaiyang Yin5State Grid Anhui Electric Power Research InstituteState Grid Anhui Electric Power Research InstituteState Grid Anhui Electric Power Research InstituteState Grid Anhui Electric Power Research InstituteState Grid Anhui Electric Power Research InstituteSchool of Electrical and Mechanical EngineeringDifferent types of partial discharge (PD) cause different damages to gas-insulated substation (GIS), so it is very important to correctly identify the type of PD for evaluating the GIS insulation condition. The traditional PD pattern recognition algorithm has the limitations of low recognition accuracy and slow recognition speed in engineering applications. To effectively diagnose the GIS PD type and safeguard the safe and reliable operation of the distribution network, a GIS PD method based on improved CBAM-ResNet was proposed in this paper. And the improved CBAM-ResNet takes advantage of the residual neural network and attention mechanism. In particular, the channel attention module and the spatial attention module are connected in parallel in the improved CBAM. The experimental results showed that the GIS PD pattern recognition method proposed herein has a recognition rate of 93.58%, 95.00%, 93.55%, and 93.88% against the four PD types. Compared with the traditional PD pattern recognition algorithm, the algorithm has the advantages of a lightweight model and more accurate recognition results, which carry better engineering application values.http://dx.doi.org/10.1155/2023/9948438 |
| spellingShingle | Di Hu Zhong Chen Wei Yang Taiyun Zhu Yanguo Ke Kaiyang Yin A GIS Partial Discharge Pattern Recognition Method Based on Improved CBAM-ResNet Journal of Electrical and Computer Engineering |
| title | A GIS Partial Discharge Pattern Recognition Method Based on Improved CBAM-ResNet |
| title_full | A GIS Partial Discharge Pattern Recognition Method Based on Improved CBAM-ResNet |
| title_fullStr | A GIS Partial Discharge Pattern Recognition Method Based on Improved CBAM-ResNet |
| title_full_unstemmed | A GIS Partial Discharge Pattern Recognition Method Based on Improved CBAM-ResNet |
| title_short | A GIS Partial Discharge Pattern Recognition Method Based on Improved CBAM-ResNet |
| title_sort | gis partial discharge pattern recognition method based on improved cbam resnet |
| url | http://dx.doi.org/10.1155/2023/9948438 |
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