Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220500032-6.doi: 10.11896/jsjkx.220500032

• Information Security • Previous Articles     Next Articles

Image Recognition Method of Transmission Line Safety Risk Assessment Based on MultidimensionalData Coupling

XU Changqian1, WANG Dong2, SU Feng2, ZHANG Jun3, BIAN Haifeng3, LI Long2   

  1. 1 State Grid Sichuan Electric Power Company,Chengdu 610000,China;
    2 State Grid Corporation of China,Beijing 100031,China;
    3 State Grid Energy Research Institute Co.,Ltd.,Beijing 100021,China
  • Online:2023-06-10 Published:2023-06-12
  • About author:XU Changqian,born in 1982,master,senior engineer.His main research interests include power system safety supervision and management.
  • Supported by:
    Science and Technology Project of State Grid Corporation of China headquarters(1400-202057415A-0-0-00).

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Abstract: Transmission lines located in high-altitude and high icing risk areas face the risk of large-area line breaking and tower falling in extreme climate.The traditional manual line patrol identification has slow speed and low accuracy,resulting in a lot of labor cost.A transmission line safety risk assessment method considering multi-dimensional image coupling driving is proposed.The icing image of key equipment is fused with the contour image of power grid operation state,so as to realize the rapid and accurate identification of relevant transmission line safety risks.Firstly,the transmission line electrical data and environmental data are coupled to generate a multi-dimensional thermal image,which can reflect the transmission line voltage offset,line load rate,ambient temperature and line icing degree in the whole system,and the line safety risk index is calculated according to the electrical data and environmental data.After that,the convolution neural network model based on MobileNet-V3 framework is built,and the generated multi-dimensional image data is used as the input of the model and the transmission line safety risk index is used as the output to train the model and generate the transmission line safety risk rapid assessment model.Finally,the model is tested on a 500kV transmission line in a province.The test results show that this method can realize the rapid and accurate assessment of transmission line safety risk.

Key words: Multidimensional data coupling, MobileNet-V3, Convolutional neural network, Ambient temperature, Line icing, Transmission line safety risk assessment

CLC Number: 

  • TM721
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