高技术通讯2017，Vol.27Issue(11)：929-937,9.DOI:10.3772/j.issn.1002-0470.2017.11-12.007
基于统计特征矢量符号值和聚类经验模态分解的短时电能质量扰动信号分析
Analysis of short time power quality disturbances based on statistic feature vector symbolic and ensemble empirical mode decomposition
摘要
Abstract
Aiming at the deficiency of the Hibert-Huang transform(HHT)method for power quality disturbance analysis, a new method for analysis of short time power quality disturbances based on the combination of statistic feaure vector symbolic(SFVS)and ensemble empirical mode decomposition(EEMD)is proposed.This method uses the SFVS algorithm for boundary detection,once the instants of transition are found,the short time power quality disturbance signal can be divided into certain segments of stationary signals and the EEMD can be applied to each segment to decompose it to effectively restrain the mode mixing.The testing results show that the proposed method can accu-rately detect the time of transition and analyse various components in the short time power quality disturbance sig -nal.关键词
短时电能质量扰动/暂态分析/统计特征矢量符号化(SFVS)/聚类经验模态分解(EEMD)/模态混叠Key words
short time power quality disturbances/transient analysis/statistic feature vector symbolic (SFVS)/ensemble empirical mode decomposition(EEMD)/mode mixing引用本文复制引用
欧阳静,张立彬,潘国兵,徐红伟,陈金鑫..基于统计特征矢量符号值和聚类经验模态分解的短时电能质量扰动信号分析[J].高技术通讯,2017,27(11):929-937,9.基金项目
863计划(2013AA050405),国家国际科技合作专项(2014DFE60020),浙江省自然科学基金(LY15E070004),和浙江省科学技术厅协同创新(2016F50010)资助项目. （2013AA050405）
