Insulator pollution detection method based on improved SVDD

SUN Lei; WU Wenhai; KE Jian; DANG Shuaitao

doi:10.13873/J.1000-9787(2019)09-0057-03

Chinese

您当前的位置：

首页 >

文章列表页 >

Insulator pollution detection method based on improved SVDD

更新时间：2025-09-11

- Insulator pollution detection method based on improved SVDD
- Vol. 38, Issue 9, Pages: 57-59(2019)
- 作者机构：
  
  西南交通大学机械工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2019)09-0057-03
  CLC： TM216;TP181
- Published：2019
- 稿件说明：
移动端阅览
[1]孙磊,吴文海,柯坚,党帅涛.基于改进SVDD的绝缘子污秽检测方法[J].传感器与微系统,2019,38(09):57-59.

SUN Lei, WU Wenhai, KE Jian, et al. Insulator pollution detection method based on improved SVDD[J]. 2019, 38(9): 57-59.
[1]孙磊,吴文海,柯坚,党帅涛.基于改进SVDD的绝缘子污秽检测方法[J].传感器与微系统,2019,38(09):57-59. DOI： 10.13873/J.1000-9787(2019)09-0057-03.

SUN Lei, WU Wenhai, KE Jian, et al. Insulator pollution detection method based on improved SVDD[J]. 2019, 38(9): 57-59. DOI： 10.13873/J.1000-9787(2019)09-0057-03.

摘要

为了解决支持向量数据描述在应用于绝缘子污秽检测时

因边缘样本区分困难而出现的误检与漏检

提出一种双超球数据域描述模型。在支持向量数据描述小包围球的基础上

设计一个大包围球

通过双包围球把数据分割成3个部分:正常区域、异常区域、拒绝区域。引入拒绝区域放弃对难以区分的样本进行决策。实验结果验证了该方法可以有效降低误检与漏检。

Abstract

In order to solve the problem that while support vector data description is applied in insulator contamination detection

due to edge samples is difficult to distinguish

so false detection and missed detection occur

a data domain description model using double surround hyper sphere is proposed. This method designs a large bounding sphere on the basis of supporting vector data to describe the small bounding sphere. The data are divided into three parts by double bounding sphere: normal area

abnormal area

and rejected area. The rejected area is introduced to give up decisions on the samples which are hard to distinguish. Experimental results show that this method can effectively reduce false detection and missing detection.

关键词

Keywords

references

可见光图像颜色特征与支持向量机相结合的绝缘子污秽状态识别方法 [J]. 金立军,曹培,胡娟. 高压电器 . 2015(02)

航拍图像中绝缘子的识别与故障诊断 [J]. 姜浩然,金立军,闫书佳. 机电工程 . 2015(02)

绝缘子污闪的形成机理及预防措施 [J]. 梁志豪. 广东电力 . 2013(06)

复合绝缘子故障检测方法的有效性分析 [J]. 律方成,刘杰,石鹏,汪佛池,熊俊,李聪. 高压电器 . 2013(05)

基于航拍图像的绝缘子表面状态检测 [J]. 葛玉敏,李宝树,赵书涛,梁爽. 高压电器 . 2010(04)

瓷绝缘子防污闪技术的研究 [J]. 李彦吉,王兰河,杜心康. 铁道标准设计 . 2008(11)

Robust support vector data description for outlier detection with noise or uncertain data [J] . Guijun Chen,Xueying Zhang,Zizhong John Wang,Fenglian Li. Knowledge-Based Systems . 2015

Density weighted support vector data description [J] . Myungraee Cha,Jun Seok Kim,Jun-Geol Baek. Expert Systems With Applications . 2014 (7)

Support Vector Data Description [J] . David M.J. Tax,Robert P.W. Duin. Machine Learning . 2004 (1)

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Identification of insulator contamination grade based on visible light image recognition

Influence of surface roughness on impulse flashover characteristics of substation insulator

A multi-scale insulator tracking algorithm based on compressive sensing

Design of on-line monitoring device of catenary insulator leakage current

Study on on-line monitoring of surface charge of insulator based on generalized probe

Related Author

DENG Fangming

XIAO Jianhua

LIU Ziying

LI Chuan

WANG Ke

MA Yi

TAN Xiang-yu

ZHANG Chang-sheng

Related Institution

School of Electrical and Automation Engineering,East China Jiaotong University

Faculty of Information Engineering and Automation,Kunming University of Science and Technology

Yunnan Power Grid Electric Power Research Institute Co Ltd

School of Automation Engineering,University of Electronic Science and Technology of China

School of Electrical Engineering,Southwest Jiaotong University

⁰