基于深度神经网络的多电平逆变器故障诊断

徐继伟; 宋保业; 公茂法

徐继伟,宋保业,公茂法.基于深度神经网络的多电平逆变器故障诊断[J].电测与仪表,2019,56(2):123-128.
Xu Jiwei,Song Baoye,Gong Maofa.Fault Diagnosis of Multilevel Inverter based on Deep Neural Network[J].Electrical Measurement & Instrumentation,2019,56(2):123-128.

基于深度神经网络的多电平逆变器故障诊断

Fault Diagnosis of Multilevel Inverter based on Deep Neural Network

DOI：

中文关键词: 深度神经网络故障诊断堆栈自编码器多电平逆变器

英文关键词:Deep neural network, fault diagnosis, stacked autoencoder, multilevel inverter

基金项目:国家自然科学基金项目（ 61703242）

作者中文名	作者英文名	单位
徐继伟	Xu Jiwei	山东科技大学电气与自动化工程学院
宋保业	Song Baoye	山东科技大学电气与自动化工程学院
公茂法	Gong Maofa	山东科技大学电气与自动化工程学院

摘要点击次数: 2299

中文摘要:

为解决多电平逆变器的故障诊断问题,本文提出了一种基于深度神经网络的多电平级联H桥逆变器的故障诊断方法。首先,介绍了多电平级联H桥逆变器的故障模型；然后,采用基于堆栈自编码器的深度神经网络直接从故障原始数据中进行故障特征提取；最后,运用SOFTMAX分类器对故障特征数据进行分类从而实现多电平逆变器的故障检测与诊断。基于MATLAB/Simulink对本文提出的方法进行了仿真实验,该方法与传统的智能诊断算法相比具有更高的准确率和更好的鲁棒性。

英文摘要:

To deal with the issue of fault diagnosis of multilevel inverter, a deep neural network based approach is proposed for the fault diagnosis of multilevel cascade H-bridge inverter. Firstly, the fault model of multilevel cascade H-bridge inverter is introduced. Then, the fault features are extracted directly from the original fault signals by using the stacked autoencoder based deep neural network. Finally, the SOFTMAX classifier is used for the classification of the fault features to carry out the fault detection and diagnosis of multilevel inverter. Several simulation experiments are implemented based on MATLAB/Simulink to test the performance of the proposed approach, which is superior to other traditional intelligent fault diagnosis algorithms on both accuracy and robustness.

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