电力系统保护与控制2011，Vol.39Issue(14)：40-46,7.
基于半监督学习的XLPE电缆局部放电模式识别研究
Pattern recognition of partial discharge in XLPE cable based on semi supervised learning
姚林朋 1王辉 1钱勇 1黄成军 1郑文栋 1江秀臣1
作者信息
- 1. 上海交通大学电气工程系,上海,200240
- 折叠
摘要
Abstract
In the research ofpattern recognition on partial discharge (PD) in XLPE cable, insufficient labelled clata may lead to low recognition rate. To solve the problem consistency model (CM) based on semi supervised learning (SSL) theory is introduced.Twenty types of statistical characteristics are extracted from PD pulse sequence from four typical models of insulation defects in a XLPE power cable. A comparison between CM based on SSL and supervised methods (J48, k Nearest Neighbor and BP Neural Network) is conducted, and CM is optimized using principal component analysis. The comparison result shows that CM method takes full advantage of both diversified characteristic information from manually labelled data and distribution information from unlabelled data to enhance the performance of the classifier and improve the recognition rate efficiently. Principal component analysis method can reduce the characteristic dimension of samples and speed up the algorithm of semi supervised learning.关键词
XLPE电缆/局部放电/半监督学习/模式识别/主成分分析Key words
XLPE cable/ partial discharge/ semi supervised learning/ pattern recognition/ principal component analysis分类
信息技术与安全科学引用本文复制引用
姚林朋,王辉,钱勇,黄成军,郑文栋,江秀臣..基于半监督学习的XLPE电缆局部放电模式识别研究[J].电力系统保护与控制,2011,39(14):40-46,7.
