中国电力 ›› 2014, Vol. 47 ›› Issue (1): 1-7.DOI: 10.11930/j.issn.1004-9649.2014.1.1.6

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采用数字图像处理方法对架空输电导线散股的研究

杨智勇, 王伟, 苏帆, 吴功平, 白玉成, 严宇   

  1. 武汉大学 动力与机械学院,湖北 武汉 430072
  • 收稿日期:2013-11-03 出版日期:2014-01-31 发布日期:2015-12-18
  • 作者简介:杨智勇(1987-),男,湖北汉川人,博士研究生,从事机器人智能控制系统研究。E-mail: whuww@whu.edu.cn
  • 基金资助:
    国家高技术研究发展计划(863计划)资助项目(2006AA04Z202)

Research on Untwisted Strand of Overhead Transmission Lines Based on Digital Image Processing Method

YANG Zhi-yong, WANG Wei, SU Fan, WU Gong-ping, BAI Yu-cheng, YAN Yu   

  1. School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
  • Received:2013-11-03 Online:2014-01-31 Published:2015-12-18
  • Supported by:
    This work is supported by National High Technology Research and Development Program of China (863 Program) (2006AA04Z202)

摘要: 架空输电导线受到施工破坏和外界环境长期作用容易产生散股,甚至断股现象,及时对其进行诊断和检修对于保证电网安全运行具有很大意义。采用巡检机器人搭载视觉检测装置巡检是现代输电线路状态检修的一种有效方式。在分析导线纹理约束及分布特征的基础上,建立了一种基于纹理斜率分布的散股判定模型,提出了一种基于数字图像处理的架空输电导线散股自动诊断方法。该方法通过对机器人拍摄的视频数据进行处理,提取图像中导线,计算导线的纹理特征分布并利用判定模型给出图像诊断结果。实验研究表明,该方法能快速检测导线的散股故障信息,能满足在线巡检的实时性要求。

关键词: 巡检机器人, 可见光图像, 纹理特征, 散股

Abstract: Due to the stringing construction damage and the long-term effects of the external environment, untwisted strands and even broken strands are prone to occur in overhead transmission lines. Timely diagnosis and overhaul of the defects are therefore very significant for the power grid security. Inspection by robot equipped with visual detection devices is an effective method in modern condition-based maintenance of power transmission lines. On the basis of analyzing the constraints and the distribution of transmission line textures, an untwisted strand judging model is built based on the texture slope distribution and an automatic diagnosis method of the untwisted strands of overhead transmission lines is proposed based on digital image processing. This method gives the results of image diagnosis by using the judging model after calculating the texture characteristic distribution of the transmission lines which are extracted from the image taken by the robot. Experiment shows that this method can quickly detect the fault information of the untwisted strands, and satisfy the real-time requirement of online inspection.

Key words: inspection robot, visual image, texture characteristics, untwisted strand

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