[1]姜艳姝,吴 迪,王伟亮. 基于 BP 神经网络的整流电路的故障诊断[J].哈尔滨理工大学学报,2018,(02):35-39.[doi:10. 15938/j. jhust. 2018. 02. 007]
 JIANG Yan-shu,WU Di,WANG Wei-liang. The Rectifiercircuit Fault Diagnosis Based on the BP Neural Network[J].哈尔滨理工大学学报,2018,(02):35-39.[doi:10. 15938/j. jhust. 2018. 02. 007]
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 基于 BP 神经网络的整流电路的故障诊断
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《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

卷:
期数:
2018年02期
页码:
35-39
栏目:
材料科学与工程
出版日期:
2018-04-25

文章信息/Info

Title:
 The Rectifiercircuit Fault Diagnosis Based on the BP Neural Network
文章编号:
1007-2683( 2018) 02-0035-05
作者:
 姜艳姝 吴 迪 王伟亮
( 哈尔滨理工大学 自动化学院,黑龙江 哈尔滨 150080)
Author(s):
 JIANG Yan-shu WU Di WANG Wei-liang
 ( School of Automation,Harbin University of Science and Technology,Harbin 150080,China)
关键词:
 电力电子电路故障诊断神经网络
Keywords:
 power electronic circuit fault diagnosis the neural network
分类号:
TP183; TN707
DOI:
10. 15938/j. jhust. 2018. 02. 007
文献标志码:
A
摘要:
 :新型电力电子产品的不断呈现以及对系统各类品质要求的日益增加,使得电力电子电 路的在线故障诊断必然成为一个急需解决的问题。针对这一问题,采用 Matlab 仿真软件建立仿真 模型,获取输出电压 ud,并用傅里叶分析法提取出直流分量、基波幅值、二次谐波以及三次谐波幅 值。将其归一化后输入到 BP 网络中,获取具有编码特征的6 个数字,从而确定故障部位及故障 点。应用上述方法,以三相桥式整流电路为例进行了仿真实验,其测试误差可达到10 -4。通过实 验验证表明:该方法与其他诊断方法相比具有诊断率高、可靠性强、适用范围广等优点
Abstract:
 Due to the new power electronic products improving and many qualities are growing,making the online fault diagnosis of power electronic circuit becomes more important. In this paper,simulation model is established by using MATLAB to obtain output voltage. Then we use the Fourier analysis method to extract the DC component,the amplitude,the second harmonic and harmonic amplitude three times. After these values are normalized,we set them into the BP network to receive six numbers in order to determine fault location and fault point. Taking three phase bridge rectifier circuit as an example with this method,the test error value is less than 10 -4. The experiment result shows that this method has merit of higher diagnostic rate,higher reliability and widely application.

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更新日期/Last Update: 2018-06-30