电工技术学报2011，Vol.26Issue(8)：198-204,7.
基于多标签RBF神经网络的电能质量复合扰动分类方法
Recognition of Multiple Power Quality Disturbances Using Multi-Label RBF Neural Networks
摘要
Abstract
A multi-label ranking learning method named ML-RBF is designed to identify the type of multiple power quality disturbances based on RBF neural networks and C-means clustering algorithm.Firstly,several common power quality disturbances and their compound ones are decomposed by discrete wavelet transform,and the norm energy entropy of the wavelet coefficients of each level are extracted as eigenvectors.And then,the eigenvectors are mapped into the input of the RBF neural networks using C-means clustering algorithm.Finally,the type of multiple power quality disturbances is predicted through the RBF neural networks.The simulation results show that ML-RBF can recognize the multiple power quality disturbances effectively under different noise conditions.关键词
电能质量/多标签分类/径向基函数/小波变换/C-均值聚类Key words
Power quality/multi-label classification/RBF/wavelet transform/C-means clustering分类
动力与电气工程引用本文复制引用
管春,周雒维,卢伟国..基于多标签RBF神经网络的电能质量复合扰动分类方法[J].电工技术学报,2011,26(8):198-204,7.基金项目
国家自然科学基金 （）
重庆邮电大学自然科学基金（A2009-41）资助项目 （）
