Automated monitoring of high-voltage transmission lines based on machine vision
This paper takes the automated monitoring of high-voltage transmission lines as the entry point and constructs an automated monitoring model of high-voltage transmission lines based on machine vision. Through the transmission equipment recognition algorithm to identify the type and location of the e...
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns-2024-0077 |
| _version_ | 1797303211221057536 |
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| author | Wang Moqi |
| author_facet | Wang Moqi |
| author_sort | Wang Moqi |
| collection | DOAJ |
| description | This paper takes the automated monitoring of high-voltage transmission lines as the entry point and constructs an automated monitoring model of high-voltage transmission lines based on machine vision. Through the transmission equipment recognition algorithm to identify the type and location of the equipment in the inspection image, using image processing methods for the collected high-voltage transmission line image color image grayscaling and grayscale image stretching, and then grayscale image smoothing and segmentation, so as to achieve the identification of the location of high-voltage transmission line fault location. Simulation test for image processing and transmission equipment identification methods, and then carry out automated monitoring experiments with high-voltage transmission line conductor stranding as an example to verify the feasibility and reliability of the model in this paper. The results show that for the automatic detection of individual conductor strand breakage, the method of this paper detects the number of strand breakage is only 1-2 less or more than the actual number of strand breakage; there is a certain difference, but basically in line with the actual situation, and the method of this paper, on average, detects the efficiency of the method is faster than the traditional method by 5.69~7.18 s/m. The research to enhance the quality of automated monitoring of high-voltage power transmission lines has a certain application value. |
| first_indexed | 2024-03-07T23:49:35Z |
| format | Article |
| id | doaj.art-dc35f9be33344ca9a8ae6ff0f8b91ffb |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-07T23:49:35Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-dc35f9be33344ca9a8ae6ff0f8b91ffb2024-02-19T09:03:34ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns-2024-0077Automated monitoring of high-voltage transmission lines based on machine visionWang Moqi01Tangshan University, Tangshan, Hebei, 063000, China.This paper takes the automated monitoring of high-voltage transmission lines as the entry point and constructs an automated monitoring model of high-voltage transmission lines based on machine vision. Through the transmission equipment recognition algorithm to identify the type and location of the equipment in the inspection image, using image processing methods for the collected high-voltage transmission line image color image grayscaling and grayscale image stretching, and then grayscale image smoothing and segmentation, so as to achieve the identification of the location of high-voltage transmission line fault location. Simulation test for image processing and transmission equipment identification methods, and then carry out automated monitoring experiments with high-voltage transmission line conductor stranding as an example to verify the feasibility and reliability of the model in this paper. The results show that for the automatic detection of individual conductor strand breakage, the method of this paper detects the number of strand breakage is only 1-2 less or more than the actual number of strand breakage; there is a certain difference, but basically in line with the actual situation, and the method of this paper, on average, detects the efficiency of the method is faster than the traditional method by 5.69~7.18 s/m. The research to enhance the quality of automated monitoring of high-voltage power transmission lines has a certain application value.https://doi.org/10.2478/amns-2024-0077machine visiontransmission equipment recognitionimage processingautomated monitoringhigh voltage transmission lines18b20 |
| spellingShingle | Wang Moqi Automated monitoring of high-voltage transmission lines based on machine vision Applied Mathematics and Nonlinear Sciences machine vision transmission equipment recognition image processing automated monitoring high voltage transmission lines 18b20 |
| title | Automated monitoring of high-voltage transmission lines based on machine vision |
| title_full | Automated monitoring of high-voltage transmission lines based on machine vision |
| title_fullStr | Automated monitoring of high-voltage transmission lines based on machine vision |
| title_full_unstemmed | Automated monitoring of high-voltage transmission lines based on machine vision |
| title_short | Automated monitoring of high-voltage transmission lines based on machine vision |
| title_sort | automated monitoring of high voltage transmission lines based on machine vision |
| topic | machine vision transmission equipment recognition image processing automated monitoring high voltage transmission lines 18b20 |
| url | https://doi.org/10.2478/amns-2024-0077 |
| work_keys_str_mv | AT wangmoqi automatedmonitoringofhighvoltagetransmissionlinesbasedonmachinevision |