E3S Web Conf.
Volume 252, 20212021 International Conference on Power Grid System and Green Energy (PGSGE 2021)
|Number of page(s)||5|
|Section||Power Control Technology and Smart Grid Application|
|Published online||23 April 2021|
Monitoring Method of Transmission Line Breaking Prevention Based on Deep Learning
1 Beijing Fibrlink Communications Co., Ltd, Beijing 100071, China ;
2 State Grid Information & Telecommunication Group Co., Ltd, Beijing 102211, China
* Corresponding author: email@example.com
With the rapid development of the national economy, the national power consumption level continues to increase, which puts forward higher requirements on the power supply guarantee capacity of the power grid system. The distribution range of the transmission line is wide and densely, most lines are exposed to the unguarded field without any shielding or protective measures, which are vulnerable to man-made destruction or natural disasters. Therefore, it is very important for the early monitoring and prevention of the external force breaking of the transmission lines. The method for preventing external breakage of transmission lines based on deep learning proposed in this paper utilizes the video data collected by the cameras erected on the transmission line roads to perform feature extraction and learning through 3D CNN and LSTM networks, and obtains a monitoring model for external breakage prevention of transmission lines. The model was tested on public data sets and verified that it has a good performance in the field of transmission lines against external damage. The method in this paper makes full use of the existing video acquisition equipment, and the process does not require human intervention, which greatly reduces the cost of line monitoring and the hidden dangers of accidents.
© The Authors, published by EDP Sciences, 2021
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.
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