Monitoring Method of Transmission Line Breaking Prevention Based on Deep Learning
| Autor: | Deng Wei, Wang Ben, Jiang Yan, Wang Guanyao, Li Qiang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Deep learning Feature extraction Real-time computing 0211 other engineering and technologies Process (computing) 02 engineering and technology 010501 environmental sciences 01 natural sciences Field (computer science) Power (physics) Environmental sciences Electric power transmission Transmission line Line (geometry) GE1-350 021108 energy Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | E3S Web of Conferences, Vol 252, p 01024 (2021) |
| ISSN: | 2267-1242 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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