基于改进聚类方式的牵引负荷分类方法  

Traction Load Classification Method Based on Improved Clustering Method

作　　者：张丽艳[1] 陈映月 韩正庆[1] ZHANG Liyan;CHEN Yingyue;HAN Zhengqing(School of Electrical Engineering,Southwest Jiaotong University,Chengdu 610031,China)

机构地区：[1]西南交通大学电气工程学院

出　　处：《西南交通大学学报》2020年第1期F0002-F0002,共1页Journal of Southwest Jiaotong University

基　　金：国家自然科学基金(51777174)

摘　　要：基于大量的牵引负荷实测数据,计算分析馈线电流带电有效系数、最大值、平均值、95%值以及1~5阶样本矩,并以这些特征量为聚类指标,用改进的自适应模糊C均值聚类方法,自动获取最佳聚类数,实现牵引负荷的有效分类,最后以非参数核密度估计方法对牵引负荷概率密度函数进行拟合。In order to obtain more accurate traction load classification,based on a large amount of measured traction load data,an improved fuzzy C-means clustering method is proposed,which can automatically obtain the best classification number.A charged effective coefficient,the maximum value,the average value,the value of 95%and one to five order moments were chosen as clustering indicators to classify feeder current.Then the probability density function of traction loads was fitted using non-parametric kernel density estimation,and the probability distribution model of each feeder current type was obtained.The results show that the characteristic parameters and probability distributions of the traction loads that were clustered together are.

关 键 词：非参数核密度估计 牵引负荷 概率密度函数 聚类指标 有效系数 最佳聚类数 改进聚类 馈线电流 

分 类 号：U221[交通运输工程—道路与铁道工程]