Partial discharge defect recognition method of switchgear based on cloud-edge collaborative deep learning
Abstract To address the limitations of traditional partial discharge (PD) detection methods for switchgear, which fail to meet the requirements for real-time monitoring, rapid assessment, sample fusion, and joint analysis in practical applications, a joint PD recognition method of switchgear based o...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-81478-9 |
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| author | Zhijie Jia Songhai Fan Zhichuan Wang Shuai Shao Dameng He |
| author_facet | Zhijie Jia Songhai Fan Zhichuan Wang Shuai Shao Dameng He |
| author_sort | Zhijie Jia |
| collection | DOAJ |
| description | Abstract To address the limitations of traditional partial discharge (PD) detection methods for switchgear, which fail to meet the requirements for real-time monitoring, rapid assessment, sample fusion, and joint analysis in practical applications, a joint PD recognition method of switchgear based on edge computing and deep learning is proposed. An edge collaborative defect identification architecture for switchgear is constructed, which includes the terminal device side, terminal collection side, edge-computing side, and cloud-computing side. The PD signal of switchgear is extracted based on UHF sensor and broadband pulse current sensor on the terminal collection side. Multidimensional features are obtained from these signals and a high-dimensional feature space is constructed based on feature extraction and dimensionality reduction on the edge-computing side. On the cloud side, the deep belief network (DBN)-based switchgear PD defect identification method is proposed and the PD samples acquired on the edge side are transmitted in real time to the cloud for training. Upon completion of the training, the resulting model is transmitted back to the edge side for inference, thereby facilitating real-time joint analysis of PD defects across multiple switchgear units. Verification of the proposed method is conducted using PD samples simulated in the laboratory. The results indicate that the DBN proposed in this paper can recognize PDs in switchgear with an accuracy of 88.03%, and under the edge computing architecture, the training time of the switchgear PD defect type classifier can be reduced by 44.28%, overcoming the challenges associated with traditional diagnostic models, which are characterized by long training durations, low identification efficiency, and weak collaborative analysis capabilities. |
| format | Article |
| id | doaj-art-880370042d2a4cb4a368d92047a24ce8 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-880370042d2a4cb4a368d92047a24ce82025-08-20T03:07:41ZengNature PortfolioScientific Reports2045-23222025-03-0115111110.1038/s41598-024-81478-9Partial discharge defect recognition method of switchgear based on cloud-edge collaborative deep learningZhijie Jia0Songhai Fan1Zhichuan Wang2Shuai Shao3Dameng He4State Grid Sichuan Electric Power Research InstituteState Grid Sichuan Electric Power Research InstituteState Grid Sichuan Electric Power CompanyState Grid Sichuan Maintenance CompanyState Grid Sichuan Maintenance CompanyAbstract To address the limitations of traditional partial discharge (PD) detection methods for switchgear, which fail to meet the requirements for real-time monitoring, rapid assessment, sample fusion, and joint analysis in practical applications, a joint PD recognition method of switchgear based on edge computing and deep learning is proposed. An edge collaborative defect identification architecture for switchgear is constructed, which includes the terminal device side, terminal collection side, edge-computing side, and cloud-computing side. The PD signal of switchgear is extracted based on UHF sensor and broadband pulse current sensor on the terminal collection side. Multidimensional features are obtained from these signals and a high-dimensional feature space is constructed based on feature extraction and dimensionality reduction on the edge-computing side. On the cloud side, the deep belief network (DBN)-based switchgear PD defect identification method is proposed and the PD samples acquired on the edge side are transmitted in real time to the cloud for training. Upon completion of the training, the resulting model is transmitted back to the edge side for inference, thereby facilitating real-time joint analysis of PD defects across multiple switchgear units. Verification of the proposed method is conducted using PD samples simulated in the laboratory. The results indicate that the DBN proposed in this paper can recognize PDs in switchgear with an accuracy of 88.03%, and under the edge computing architecture, the training time of the switchgear PD defect type classifier can be reduced by 44.28%, overcoming the challenges associated with traditional diagnostic models, which are characterized by long training durations, low identification efficiency, and weak collaborative analysis capabilities.https://doi.org/10.1038/s41598-024-81478-9SwitchgearPartial dischargeEdge computingLocal linear embeddingDeep belief network |
| spellingShingle | Zhijie Jia Songhai Fan Zhichuan Wang Shuai Shao Dameng He Partial discharge defect recognition method of switchgear based on cloud-edge collaborative deep learning Scientific Reports Switchgear Partial discharge Edge computing Local linear embedding Deep belief network |
| title | Partial discharge defect recognition method of switchgear based on cloud-edge collaborative deep learning |
| title_full | Partial discharge defect recognition method of switchgear based on cloud-edge collaborative deep learning |
| title_fullStr | Partial discharge defect recognition method of switchgear based on cloud-edge collaborative deep learning |
| title_full_unstemmed | Partial discharge defect recognition method of switchgear based on cloud-edge collaborative deep learning |
| title_short | Partial discharge defect recognition method of switchgear based on cloud-edge collaborative deep learning |
| title_sort | partial discharge defect recognition method of switchgear based on cloud edge collaborative deep learning |
| topic | Switchgear Partial discharge Edge computing Local linear embedding Deep belief network |
| url | https://doi.org/10.1038/s41598-024-81478-9 |
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