基于改进的VMD和CNN神经网络的光伏逆变器软故障诊断方法研究  

Research on soft fault diagnosis method of PV inverter based on improved VMD and CNN neural network

作　　者：姜媛媛[1] 张书婷 Jiang Yuanyuan;Zhang Shuting(Anhui University of Science and Technology,Huainan 232001,Anhui,China)

机构地区：[1]安徽理工大学,安徽淮南232001

出　　处：《电测与仪表》2021年第2期158-163,共6页Electrical Measurement & Instrumentation

基　　金：国家自然科学基金资助项目(51604011)。

摘　　要：针对光伏发电系统中光伏逆变器电路复杂,出现故障时间短等问题,文中提出一种基于改进的变分模态分解和卷积神经网络相结合的故障诊断方法,可有效地解决故障特征提取困难,特征参数奇异性差,以及由于特征参数差而引起的故障诊断率低等问题。利用SIMULINK建立光伏逆变器软故障模型,并采集相关参数作为样本;使用VMD对参数进行变分模态分解,得到若干分量,并且利用小波变换提取各模态分量的小波能量,获得故障特征值并降维;用卷积神经网络CNN进行故障诊断,并用其结果与传统的VMD-CNN神经网络、VMD-BP神经网络的诊断结果进行比较,验证了此神经网络用于光伏逆变器软故障诊断的正确性和精确性,具有一定的优势。For photovoltaic inverter circuit in the photovoltaic system is complex,and the failure time is short,this paper puts forward a kind of fault diagnosis methodbased on the improved variational mode decomposition(VMD) and the convolutional neural network(CNN),which can effectively solve the problems that the fault feature extraction is difficult,characteristic parameters of singularity is poor,and the low fault diagnosis rate caused by poor characteristic parameters.Firstly,the software SIMULINK is used to establish the soft fault model of photovoltaic inverter,and relevant parameters are collected as samples.Then,VMD is used for variational modal decomposition of the parameters to obtain some components,and wavelet transform is used to extract the wavelet energy of each modal component to obtain the fault characteristic value and reduce the dimension.Finally,the CNN is used for fault diagnosis,and the results are compared with the traditional VMD-CNN neural network and VMD-BP neural network,which verifies the correctness and accuracy of the softfault diagnosis of photovoltaic inverter using the network,and has certain advantages.

关 键 词：光伏逆变器 改进的变分模态分解 卷积神经网络 故障诊断 

分 类 号：TM714[电气工程—电力系统及自动化] TM93[电气工程—电力电子与电力传动]