基于优化VMD和能量相对熵的地铁车载电容状态识别
Capacitance status identification of subway vehicles based on optimized VMD and energy relative entropy
李小波 1曹烁 1冯秋峰 1白晏年 1杨志豪 1张浩1
作者信息
- 1. 上海工程技术大学 城市轨道交通学院 上海 201620
- 折叠
摘要
针对地铁车载电容性能退化无明显征兆这一现状,提出一种基于优化变分模态分解(variational mode decomposition,VMD)和能量相对熵的电容状态识别方法.通过Matlab仿真建模,提取电容在正常状态和不同退化情况下负载侧输出电压信号并利用优化VMD进行分解得到若干模态分量.将其作为特征样本,对上述各状态的本征模态分量的能量特征向量进行相对熵分析,得到电容退化识别阈值.实际应用时,将待测电路的能量相对熵值与识别阈值进行比较从而完成电容状态识别.分析结果表明,此方法简单有效,判断正确率为93.3%.
Abstract
Aiming at the problem that there is no obvious symptom of capacitance performance degradation on subway vehicles,a capacitance status identification method based on optimized variational mode decomposition(VMD)and energy relative entropy was proposed.By establishing a Matlab circuit model,the output voltage signals of the capacitance at normal status or different degradation conditions were extracted,then the characteristic samples were obtained by decomposition of optimized VMD.And the relative entropy analysis of the energy eigenvectors of the eigenmode components at the above status was carried out to obtain the identification threshold of capacitance degradation.In practical application,the relative entropy value of the energy of the circuit under test and the normal status was compared with the identification threshold to complete the capacitance status identification.The analysis result shows that this method can identify the capacitance status simply and effectively,and the accuracy is 93.3%.
关键词
地铁车辆/车载电容/状态识别/优化变分模态分解/能量相对熵Key words
subway vehicle/vehicle capacitance/status identification/optimized variational mode decomposition(VMD)/energy relative entropy引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据