Fault Diagnosis for Transformers Based on FRVM and DBN

4th International Conference on Advances in Energy Resources and Environment Engineering

能源学;生态环境科学

Chen, Renbo^1 ; Yuan, Yue^2 ; Zhang, Zhiqiang^2 ; Chen, Xin^2 ; He, Feiyu^2

Mianyang Power Supply Company of State Grid, Mianyang, Sichuan

621000, China^1

Sichuan University School of Electrical Engineering and Information, Chengdu, Sichuan

610065, China^2

关键词: Dissolved gas analyses (DGA);    Feature information;    Hybrid model;    Input parameter;    Insulation oil;    Mapping relationships;    Multiple faults;    Potential faults;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/237/6/062030/pdf
DOI  :  10.1088/1755-1315/237/6/062030

学科分类：环境科学（综合）

来源: IOP

【 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.