| Issue |
E3S Web Conf.
Volume 185, 2020
2020 International Conference on Energy, Environment and Bioengineering (ICEEB 2020)
|
|
|---|---|---|
| Article Number | 01057 | |
| Number of page(s) | 3 | |
| Section | Energy Engineering and Power System | |
| DOI | https://doi.org/10.1051/e3sconf/202018501057 | |
| Published online | 01 September 2020 | |
Research and analysis on defect detection of semi-conductive layer of high voltage cable
State grid Hunan electric power co, LTD. Electric power research institute, Changsha, Hunan 410000, China
* Corresponding author: 604208086@qq.com
As a component of the Internet of things, high-voltage cables are the power supply infrastructure for the modern development of cities. The operation experience shows that the high-voltage cable has been broken down many times due to the defective operation. At present, due to the limitation of detection technology, the research on detection and identification of defects in high-voltage cables is progressing slowly. Therefore, a new DR technology based on X-ray digital imaging is proposed in this paper to realize real-time detection of defects in the semi-conductive buffer layer of high-voltage cables, and intelligent detection of DR images of high-voltage cables by using image depth processing technology to realize intelligent identification of defects in the buffer layer of power cables. The results show that using the new DR technique proposed in this paper, the accurate and intuitive DR image of high-voltage cable can be obtained quickly, and the intelligent identification of defects can be realized.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.