Fault feature extraction for wind turbine based on PCA

被引：0
|
作者：
Chen Tao [1 ]
Xu Xiaoli [1 ]
机构：
[1] Beijing Informat Sci & Technol, Minist Educ, Key Lab Modern Measurement & Control Technol, Beijing 100192, Peoples R China
来源：
PROCEEDINGS OF THE FIFTH INTERNATIONAL SYMPOSIUM ON TEST AUTOMATION & INSTRUMENTATION, VOLS 1 AND 2 | 2014年
关键词：
feature extraction; wind turbine generator; PCA; KPCA; PRINCIPAL COMPONENT ANALYSIS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Wind turbine is the key equipment in energy transformation.In this paper, the principal component and the kernel principal component are studied in fault diagnosis of wind turbine.Principle and characteristics of PCA and KPCA are analyzed. And experiment of fault diagnosis in wind turbine generator shows that the PCA has better performance in feature extraction application.
引用
收藏
页码：82 / 85
页数：4
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