文章基本信息

标题：Fault Diagnosis for Transformers Based on FRVM and DBN
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作者：Renbo Chen ; Renbo Chen ; Yue Yuan 等
期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：237
期号：6
页码：062030
DOI：10.1088/1755-1315/237/6/062030
出版社：IOP Publishing
摘要：Dissolved gas analysis (DGA) of insulation oil is widely used in potential fault analysis for transformers. In order to improve the accuracy of fault diagnosis, a hybrid model which combines the FRVM with the depth belief network (DBN) is proposed to establish the mapping relationship between gas and fault types. Considering that DBN needs to extract a huge amount of feature information, this paper uses FRVM to separate the discharge and overheating faults, and then uses DBN to realize further fault diagnosis. The diagnosis accuracy is studied when IEC ratio, Rogers ratio, Dornenburg ratio and non-cod ratios are used as input parameters, and the results show that the correct rate of diagnosis is highest when the non-cod ratios are used as characteristic parameter. In addition, the method has better performance compared with single DBN, support vector machine and artificial neural network, and it has the ability to diagnose multiple faults.