基于径向基概率神经网络的输电导线缺陷状态识别

黄新波; 章小玲; 张烨; 杨璐雅; 刘成; 李文静

doi:10.7500/AEPS20190122004

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基于径向基概率神经网络的输电导线缺陷状态识别

研制与开发 | 更新时间：2025-06-16

- 基于径向基概率神经网络的输电导线缺陷状态识别
- State Identification of Transmission Line Defect Based on Radial Basis Probabilistic Neural Network
- 电力系统自动化-方正平台 2020年44卷第3期页码：201-210
- 作者机构：
  
  1. 西安工程大学电子信息学院，陕西省西安市 710048
  2. 重庆水利电力职业技术学院智能制造学院，重庆市 402160
- 作者简介：
  
  黄新波(1975—)，男，通信作者，博士，教授，博士生导师，主要研究方向：电气设备在线监测理论与关键技术。E-mail：huangxb1975@163.com
  章小玲(1993—)，女，硕士研究生，主要研究方向：智能电网在线监测理论与关键技术。E-mail：1187628950@qq.com
  张烨(1988—)，女，博士，讲师，主要研究方向：智能电网在线监测理论与关键技术。E-mail：286523007@qq.com
- 基金信息：
  
  陕西省教育厅专项科研计划资助项目(18JK0345);陕西省重点项目——工业领域资助项目(2018ZDXM-GY-040);重庆市教委科学技术研究项目(KJ1735449)
- DOI：10.7500/AEPS20190122004
  中图分类号：
- 收稿日期：2019-01-22，
  
  修回日期：2019-06-10，
  
  网络出版日期：2019-12-13，
  
  纸质出版日期：2020-03-25
- 稿件说明：
移动端阅览
黄新波,章小玲,张烨等.基于径向基概率神经网络的输电导线缺陷状态识别[J].电力系统自动化,2020,44(03):201-210.

HUANG Xinbo,ZHANG Xiaoling,ZHANG Ye,et al.State Identification of Transmission Line Defect Based on Radial Basis Probabilistic Neural Network[J].Automation of Electric Power Systems,2020,44(03):201-210.
黄新波,章小玲,张烨等.基于径向基概率神经网络的输电导线缺陷状态识别[J].电力系统自动化,2020,44(03):201-210. DOI： 10.7500/AEPS20190122004.

HUANG Xinbo,ZHANG Xiaoling,ZHANG Ye,et al.State Identification of Transmission Line Defect Based on Radial Basis Probabilistic Neural Network[J].Automation of Electric Power Systems,2020,44(03):201-210. DOI： 10.7500/AEPS20190122004.

摘要

输电导线作为承担电能传输任务的重要部件，及时发现其本体缺陷对指导维修避免重大电力事故的发生具有重要意义。考虑到无人机巡检中输电导线背景的复杂性和导线表面缺陷检测的困难度，提出一种基于径向基概率神经网络的输电导线缺陷状态识别方法。首先，依次采用加权色差法、最大类间方差法以及形态学滤波实现复杂背景下输电导线的准确分割。其次，将分割出的导线区域等距划分为10个导线子图像，通过Gabor滤波器获得输电导线8个角度、5个尺度的40幅纹理增强子图像，提取各个子图像的粗糙度、对比度和方向度3个纹理特征量，结合特征方差比筛选出10个强纹理特征；最后，将10个强纹理特征量作为径向基概率神经网络的输入，完成输电导线缺陷状态的识别。实验结果表明所提方法可以实现复杂背景下输电导线快速分割与缺陷状态的准确识别，为无人机巡检中输电导线的运行状态检测提供了新的思路。

Abstract

The transmission line plays an important part in power transmission task

so it is of great significance to identify its defects for the maintenance

and the severe power accidents can be avoided or decreased. For the background of images captured by unmanned aerial vehicle is very complex and difficult to be detected

a radial basis probabilistic neural network based fault location identification method for transmission lines is proposed. Firstly

the weighted color difference method

maximum interclass variance method and morphological filtering are sequentially adopted to realize the accurate segmentation of transmission lines in complicated background. Secondly

the segmented line area is equally divided into 10 line sub-images

40 texture enhancement sub-images at 8 angles and 5 dimensions of transmission lines are obtained by Gabor filter

and the roughness

contrast and orientation of each sub-image are also extracted. By the feature variance

10 strong texture features are selected and adopted as the input parameters to the radial basis probabilistic neural network for the defect identification of transmission line. The results show that both the rapid segmentation of transmission lines and the accurate identification of the defects based on the images in the complex background can be achieved by the proposed method

which provides a new idea for the operation state detection of transmission line in unmanned aerial vehicle inspection.

关键词

Keywords

references

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