Numerical Recognition Algorithm for Power Equipment Monitoring Based on Light-Resnet Convolutional Neural Network
In the smart grid, precise monitoring of the operational status of critical equipment for transmission, distribution and power supply is essential for effective online maintenance. Faced with the inefficiencies of manual recording and inspection, as well as the challenges associated with the complex...
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| Format: | Article |
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State Grid Energy Research Institute
2024-08-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202310020 |
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| author | Zhiheng KONG Chong TAN Peiyao TANG Chengbo HU Min ZHENG |
| author_facet | Zhiheng KONG Chong TAN Peiyao TANG Chengbo HU Min ZHENG |
| author_sort | Zhiheng KONG |
| collection | DOAJ |
| description | In the smart grid, precise monitoring of the operational status of critical equipment for transmission, distribution and power supply is essential for effective online maintenance. Faced with the inefficiencies of manual recording and inspection, as well as the challenges associated with the complex installation, high cost and lengthy periods required for digital upgrades of monitoring devices, a novel approach that integrates image capture devices with image processing technology has been developed. This approach, leveraging the allocation of computational resources for task distribution, introduces a Light-Resnet-based numerical recognition algorithm, which enhances network training through the optimization of the D-Add loss function, enabling remote reading of electrical equipment monitoring data. Experiments have demonstrated that Light-Resnet achieves a rigorous accuracy rate of 98.8% on the MNIST dataset with only 6090 parameters. When combined with edge computing collaboration mechanisms, it resulted in a 20.73% reduction in power consumption on the terminal side. The proposed algorithm not only proves its adaptability and efficiency in resource-constrained environments but also significantly improves the network's accuracy with design of the D-Add loss function. |
| format | Article |
| id | doaj-art-5772fcb8fe31490695c1372d67f5b07a |
| institution | OA Journals |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2024-08-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-5772fcb8fe31490695c1372d67f5b07a2025-08-20T02:04:31ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492024-08-0157820621310.11930/j.issn.1004-9649.202310020zgdl-57-03-kongzhihengNumerical Recognition Algorithm for Power Equipment Monitoring Based on Light-Resnet Convolutional Neural NetworkZhiheng KONG0Chong TAN1Peiyao TANG2Chengbo HU3Min ZHENG4Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, ChinaShanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, ChinaShanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, ChinaState Grid Jiangsu Electric Power Company Ltd. Research Institute, Nanjing 211103, ChinaShanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, ChinaIn the smart grid, precise monitoring of the operational status of critical equipment for transmission, distribution and power supply is essential for effective online maintenance. Faced with the inefficiencies of manual recording and inspection, as well as the challenges associated with the complex installation, high cost and lengthy periods required for digital upgrades of monitoring devices, a novel approach that integrates image capture devices with image processing technology has been developed. This approach, leveraging the allocation of computational resources for task distribution, introduces a Light-Resnet-based numerical recognition algorithm, which enhances network training through the optimization of the D-Add loss function, enabling remote reading of electrical equipment monitoring data. Experiments have demonstrated that Light-Resnet achieves a rigorous accuracy rate of 98.8% on the MNIST dataset with only 6090 parameters. When combined with edge computing collaboration mechanisms, it resulted in a 20.73% reduction in power consumption on the terminal side. The proposed algorithm not only proves its adaptability and efficiency in resource-constrained environments but also significantly improves the network's accuracy with design of the D-Add loss function.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202310020light-resnetd-addedge-end collaboration mechanismnumerical recognitionsmart grid |
| spellingShingle | Zhiheng KONG Chong TAN Peiyao TANG Chengbo HU Min ZHENG Numerical Recognition Algorithm for Power Equipment Monitoring Based on Light-Resnet Convolutional Neural Network Zhongguo dianli light-resnet d-add edge-end collaboration mechanism numerical recognition smart grid |
| title | Numerical Recognition Algorithm for Power Equipment Monitoring Based on Light-Resnet Convolutional Neural Network |
| title_full | Numerical Recognition Algorithm for Power Equipment Monitoring Based on Light-Resnet Convolutional Neural Network |
| title_fullStr | Numerical Recognition Algorithm for Power Equipment Monitoring Based on Light-Resnet Convolutional Neural Network |
| title_full_unstemmed | Numerical Recognition Algorithm for Power Equipment Monitoring Based on Light-Resnet Convolutional Neural Network |
| title_short | Numerical Recognition Algorithm for Power Equipment Monitoring Based on Light-Resnet Convolutional Neural Network |
| title_sort | numerical recognition algorithm for power equipment monitoring based on light resnet convolutional neural network |
| topic | light-resnet d-add edge-end collaboration mechanism numerical recognition smart grid |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202310020 |
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