计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (6A): 220500032-6.doi: 10.11896/jsjkx.220500032

• 信息安全 • 上一篇    下一篇

基于图像数据耦合识别的输电线路安全风险评估方法

徐昌前1, 王东2, 苏峰2, 张钧3, 边海峰3, 李龙2   

  1. 1 国网四川省电力公司 成都 610000;
    2 国家电网有限公司 北京 100031;
    3 国网能源研究院有限公司 北京 100021
  • 出版日期:2023-06-10 发布日期:2023-06-12
  • 通讯作者: 徐昌前(646608376@qq.com)
  • 基金资助:
    国家电网有限公司总部科技项目(1400-202057415A-0-0-00)

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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摘要： 位于高海拔、高覆冰风险地区的输电线路在极端气候下面临大面积断线和倒塔风险,传统人工巡线识别速度慢、准确度低,造成大量人力成本开销。提出一种考虑多维图像耦合驱动的输电线路安全风险评估方法,将关键设备覆冰图像与电网运行状态等高线图像进行融合识别,以实现相关输电线路安全风险快速准确辨识。首先将输电线路电气数据和环境数据耦合生成多维热力图像,生成可反映全系统内输电线路电压偏移度、线路负载率、环境温度和线路覆冰程度的多维图像数据,并根据电气数据和环境数据计算线路安全风险指标。之后,搭建基于MobileNet-V3框架的卷积神经网络模型,并将生成的多维图像数据作为该模型的输入,输电线路安全风险指标作为输出,对模型进行训练,生成输电线路安全风险快速评估模型。最后在某省500kV输电线路上对该模型进行测试,测试结果表明,该方法可实现输电线路安全风险快速准确评估。

关键词: 多维数据耦合, MobileNet-V3, 卷积神经网络, 环境温度, 线路覆冰, 输电线路安全风险评估

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

中图分类号: 

  • TM721
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