摘要
提出一种改进S变换和相关向量机相结合的电能质量扰动分类法.首先通过引入调节因子构建时频分辨率可控的改进S变换,从而提取各类扰动信号的时频特性;然后利用层次分类法与最小输出编码法构建贝叶斯相关向量机多级分类树模型,实现电能质量扰动信号的分类与识别.研究表明,该方法能在强噪声背景下获得高精度的扰动分类识别率,具备比S变换更高的时频分析能力,较支持向量机需要更少的相关向量数目,测试时间更短.
A method classifying power quality disturbances(PQD) based on modified S-transform and relevance vector machine(RVM) is presented.The modified S-transform(MST) is achieved by adding three adjustable factors to the Gaussian window function of the normal S-Transform.The adjustable factors change the velocity in which the width of the window function varies with the frequency.The PQD sample eigenvectors can be extracted accurately by using the modified S-transform with better time-frequency analysis performance than the S-Transform.Then the disturbance types are identified through the multi-lay RVM pattern recognition classifier on hierarchical categorization and minimum output coding.Numerical results show that the proposed MST-based RVM method can achieve higher classification accuracy quickly,and requires substantially fewer relevance vectors and shorter test time than the SVM classifier.
出处
《控制与决策》
EI
CSCD
北大核心
2011年第4期587-591,共5页
Control and Decision
基金
国家自然科学基金项目(60874014)
江苏省高校自然科学基金项目(10KJB470003)
镇江市科技培育项目(SH2008005)
江苏大学高级人才启动基金项目(10JDG136)
关键词
电能质量
扰动识别
改进S变换
相关向量机
支持向量机
power quality
disturbance identification
modified S-transform
relevance vector machine
support vector machine