电力系统保护与控制2011，Vol.39Issue(21)：104-109,114,7.
基于AdaBoost的局部放电综合特征决策树识别方法
Pattern recognition based on AdaBoost decision tree for partial discharge
姚林朋 1郑文栋 1钱勇 1王辉 1黄成军 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 of pattern recognition on partial discharge (PD) in GIS, the traditional decision tree method faces problems of complex structure, low recognition rate and vulnerability to noise data due to the single features and limited training pattern modes. In this paper, a method of using AdaBoost decision tree integrating with composited features is presented. Features are extracted from three aspects including statistical distribution of p-q-n diagram, moment distribution of q-t diagram and Weibull distribution parameters of q-n diagram and samples are collected from the typical discharges from high voltage needle, floating electrode, void, free particle in GIS and interferences from mobile phone and light. The influence of single features and composited features on the recognition effects of C4.5 decision tree and AdaBoost decision tree is studied. Recognition results of laboratory test and field test show that AdaBoost decision tree made with features composited with three aspects can effectively optimize the recognition rate and improve the efficiency of its time and space use.关键词
气体绝缘组合电器/超高频/局部放电/决策树/AdaBoost/C4.5/模式识别Key words
GIS/UHF/partial discharge/decision tree/AdaBoost/C4.5/pattern recognition分类
信息技术与安全科学引用本文复制引用
姚林朋,郑文栋,钱勇,王辉,黄成军,江秀臣..基于AdaBoost的局部放电综合特征决策树识别方法[J].电力系统保护与控制,2011,39(21):104-109,114,7.
