摘要
根据GIS设备绝缘缺陷放电形式和特点,设计了4种典型的GIS缺陷模型,构造了局部放电灰度谱图;针对GIS局部放电及其缺陷特点,提出一种基于局部放电图像的主分量分析-线性鉴别方法,即首先进行主分量分析,将数据从超高维空间降至低维空间,再提取统计不相关的最优鉴别矢量集,采用最小距离分类器进行模式识别,识别结果表明该方法对GIS各类模拟缺陷的正确识别率较高,效果良好.
According to the character of PD in GIS, the authors design four kinds of GIS defection models. The GIS gray intensity images are constructed based on mass specimens gathered by the ultra - high frequency and high speeds systems, Aiming at the PD characteristics and its defections, A PCA-FDA method is put forward based on PD images. The principal component analysis is employed to condense the dimension of PD images, then the optimal sets of statistically uncorrelated discriminant vectors are extracted, and the minimum distance classifier is constructed as classifier. The identified results show that this method can effectively elevated the discrimination of the four kinds of defects in GIS PD.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2006年第10期1-4,共4页
Journal of Chongqing University
基金
国家自然科学基金资助项目(50577069)
重庆市自然科学基金资助(CSTC2005BB3170)
关键词
GIS
局部放电
模式识别
主分量分析
线性鉴别分析
GIS
PD
pattern recognition
Linear Discriminant Analysis
principal component analysis
