Panoramic Assessment Method of Substation Equipment Health Status Based on Multisource Monitoring and Deep Convolution Neural Network under Edge Computing Architecture
In view of the low efficiency of the traditional manual evaluation method of substation equipment status under the background of complex environment, a panoramic evaluation method of substation equipment health status based on multisource monitoring and deep convolution neural network under edge com...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2023-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/9194712 |
| _version_ | 1797838044249718784 |
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| author | Zhu-xing Ma Li-shuo Zhang Hao Gu Zi-zhong Xin Zhe Kang Zhao-lei Wang |
| author_facet | Zhu-xing Ma Li-shuo Zhang Hao Gu Zi-zhong Xin Zhe Kang Zhao-lei Wang |
| author_sort | Zhu-xing Ma |
| collection | DOAJ |
| description | In view of the low efficiency of the traditional manual evaluation method of substation equipment status under the background of complex environment, a panoramic evaluation method of substation equipment health status based on multisource monitoring and deep convolution neural network under edge computing architecture is proposed. Firstly, a panoramic sensing system for substation equipment is built based on edge computing, and an edge computing server is deployed in the substation to process the massive data obtained from multisource monitoring nearby. Then, the improved YOLOv4 network is used to detect the equipment state in the substation, in which the Squeeze-and-Excitation attention module and deep separable convolution are used to optimize the YOLOv4 network. Finally, based on the status image of substation equipment, the health status of equipment is evaluated on the panoramic platform of substation combined with the characteristics of multisource data, and four states are divided according to the evaluation criteria. Based on the selected dataset, the experimental analysis of the proposed method is carried out. The results show that the index values of accuracy, recall, and mean precision are 91.53%, 93.07%, and 92.28%, respectively. The overall performance is better than other methods and has certain application value. |
| first_indexed | 2024-04-09T15:34:28Z |
| format | Article |
| id | doaj.art-6f1ffa8044c546ddb4f65047a136efce |
| institution | Directory Open Access Journal |
| issn | 2090-0155 |
| language | English |
| last_indexed | 2024-04-09T15:34:28Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj.art-6f1ffa8044c546ddb4f65047a136efce2023-04-28T00:00:11ZengHindawi LimitedJournal of Electrical and Computer Engineering2090-01552023-01-01202310.1155/2023/9194712Panoramic Assessment Method of Substation Equipment Health Status Based on Multisource Monitoring and Deep Convolution Neural Network under Edge Computing ArchitectureZhu-xing Ma0Li-shuo Zhang1Hao Gu2Zi-zhong Xin3Zhe Kang4Zhao-lei Wang5State Grid Hebei Extra High Voltage CompanyState Grid Hebei Extra High Voltage CompanyState Grid Hebei Extra High Voltage CompanyState Grid Hebei Extra High Voltage CompanyState Grid Hebei Extra High Voltage CompanyState Grid Hebei Extra High Voltage CompanyIn view of the low efficiency of the traditional manual evaluation method of substation equipment status under the background of complex environment, a panoramic evaluation method of substation equipment health status based on multisource monitoring and deep convolution neural network under edge computing architecture is proposed. Firstly, a panoramic sensing system for substation equipment is built based on edge computing, and an edge computing server is deployed in the substation to process the massive data obtained from multisource monitoring nearby. Then, the improved YOLOv4 network is used to detect the equipment state in the substation, in which the Squeeze-and-Excitation attention module and deep separable convolution are used to optimize the YOLOv4 network. Finally, based on the status image of substation equipment, the health status of equipment is evaluated on the panoramic platform of substation combined with the characteristics of multisource data, and four states are divided according to the evaluation criteria. Based on the selected dataset, the experimental analysis of the proposed method is carried out. The results show that the index values of accuracy, recall, and mean precision are 91.53%, 93.07%, and 92.28%, respectively. The overall performance is better than other methods and has certain application value.http://dx.doi.org/10.1155/2023/9194712 |
| spellingShingle | Zhu-xing Ma Li-shuo Zhang Hao Gu Zi-zhong Xin Zhe Kang Zhao-lei Wang Panoramic Assessment Method of Substation Equipment Health Status Based on Multisource Monitoring and Deep Convolution Neural Network under Edge Computing Architecture Journal of Electrical and Computer Engineering |
| title | Panoramic Assessment Method of Substation Equipment Health Status Based on Multisource Monitoring and Deep Convolution Neural Network under Edge Computing Architecture |
| title_full | Panoramic Assessment Method of Substation Equipment Health Status Based on Multisource Monitoring and Deep Convolution Neural Network under Edge Computing Architecture |
| title_fullStr | Panoramic Assessment Method of Substation Equipment Health Status Based on Multisource Monitoring and Deep Convolution Neural Network under Edge Computing Architecture |
| title_full_unstemmed | Panoramic Assessment Method of Substation Equipment Health Status Based on Multisource Monitoring and Deep Convolution Neural Network under Edge Computing Architecture |
| title_short | Panoramic Assessment Method of Substation Equipment Health Status Based on Multisource Monitoring and Deep Convolution Neural Network under Edge Computing Architecture |
| title_sort | panoramic assessment method of substation equipment health status based on multisource monitoring and deep convolution neural network under edge computing architecture |
| url | http://dx.doi.org/10.1155/2023/9194712 |
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