电测与仪表2017，Vol.54Issue(17)：7-10,17,5.
基于振动分析法的变压器故障分类和识别
The classification and recognition of transformer fault based on vibration analysis
摘要
Abstract
In order to achieve the fault identification and classification intuitively of transformer fault , this paper pro-poses a method of transformer fault detection method based on PCA ( principal component analysis ) and KNN ( K-Nearest Neighbor ) classification and recognition .In this paper , vibration signals from different transformer states are decomposed by EMMD ( ensemble empirical mode decomposition ) to abstract feature vectors which are projected onto a visual two-dimensional image .KNN classification is applied to verify fault classification and achieve automatic fault identification .Experimental results show that this method can achieve classification of a normal state of transformer , winding deformation and the core fault respectively , which can realize automatically pattern recognition of test sample .关键词
振动分析法/集合经验模式分解/特征矢量/主成分分析/K近邻法Key words
vibration analysis/ensemble empirical mode decomposition/feature vector/principal componentanalysis/K-Nearest Neighbor分类
信息技术与安全科学引用本文复制引用
夏玉剑,李敏,陈果,石同春,沈大千,王昕..基于振动分析法的变压器故障分类和识别[J].电测与仪表,2017,54(17):7-10,17,5.基金项目
国家自然科学基金重点项目(61533012) （61533012）
上海市自然科学基金资助项目(14ZR1421800) （14ZR1421800）
