中国电机工程学报2011，Vol.31Issue(4)：45-50,6.
多标签分类法在电能质量复合扰动分类中的应用
Application of Multi-label Classification Method to Catagorization of Multiple Power Quality Disturbances
摘要
Abstract
A new method of identifying the catagory of multiple power quality disturbances based on multi-label classification was presented. A multi-label ranking learning method named k-nearest neighbor Bayesian rule (KNN-Bayesian) was designed based on k-nearest neighbor and Bayesian methods. Firstly, several common power quality disturbances and their compound ones were decomposed by discrete wavelet transform, and the norm energy entropy of the wavelet coefficients of each level were extracted as eigenvectors. And then, the disturbances were classified using KNN-Bayesian. The simulation results show that KNN-Bayesian can recognize the multiple power quality disturbances including voltage sag, voltage swell, interruption, impulsive transient, harmonics, flicker and their compound ones effectively under different disturbance conditions.关键词
电能质量复合扰动/多标签分类/κ-近邻/小波变换/贝叶斯准则分类
信息技术与安全科学引用本文复制引用
周雒维,管春,卢伟国..多标签分类法在电能质量复合扰动分类中的应用[J].中国电机工程学报,2011,31(4):45-50,6.基金项目
国家自然科学基金项目(50807058). （50807058）
