电力系统保护与控制2012，Vol.40Issue(7)：94-99,6.
基于主成分分析和基因表达式程序设计的变压器故障诊断
Transformer fault diagnosis based on principal component analysis and gene expression programming
摘要
Abstract
A kind of Gene Expression Programming algorithm (GEP) based on Principal Component Analysis (PC A) is proposed and used for transformer fault diagnosis. The application of Principal Component Analysis can reduce the dimension of the feature vectors and eliminate the irrelevance between vectors so as to decrease the computational complexity of the diagnosis classifier and increase the training and testing accuracy. Then the obtained new sample data are trained using GEP algorithm so as to construct transformer fault diagnosis model. 170 groups of the transformer DGA data which can reflect the variety of the faults without redundancy are used to study and get the GEP classifier, while the other 130 instances are diagnosed by the GEP classifier. The experiment shows that the proposed algorithm has obviously higher diagnostic accuracy and speed than solely using GP or GEP.This work is supported by Natural Science Foundation of Hebei Province (No. E2009001392).关键词
变压器/故障诊断/基因表达式程序设计/主成分分析/DGAKey words
transformer/ fault diagnosis/ gene expression programming/ principal component analysis/ DGA分类
信息技术与安全科学引用本文复制引用
董卓,朱永利,张宇,邵宇鹰..基于主成分分析和基因表达式程序设计的变压器故障诊断[J].电力系统保护与控制,2012,40(7):94-99,6.基金项目
河北省自然科学基金资助项目(E2009001392) （E2009001392）
