一种融合FRVM和DBN的变压器故障诊断方法  

A Transformers Diagnosis Method Based on FRVM and DBN

作　　者：周步祥[1] 袁岳 林楠[2] 罗燕萍 张致强 王耀雷 ZHOU Bu-xiang;YUAN Yue;LIN Nan;LUO Yan-ping;ZHANG Zhi-qiang;WANG Yao-lei(School of Electrical Engineering and Information,Sichuan University,Chengdu 610065,China;Sichuan Power Supply Company of StateGrid,Chengdu 610041,China;Economic and Technical Research Institute of Shandong Power Supply Company of State Grid,Jinan 250000,China)

机构地区：[1]四川大学电气信息学院,四川成都610065 [2]国网四川省电力公司,四川成都610041 [3]国网山东省电力公司经济技术研究院,山东济南250000

出　　处：《水电能源科学》2019年第9期188-191,158,共5页Water Resources and Power

摘　　要：为了提高变压器故障诊断精度,提出了一种将快速相关向量机(FRVM)与深度信念网络(DBN)相结合的变压器故障诊断方法,即将油中溶解气体的比值作为输入参数,建立气体与故障类型之间的映射关系,考虑到DBN需提取的特征信息数量巨大,先用FRVM将放电和过热故障分离,减少DBN所需提取的特征信息,然后利用DBN实现进一步的故障诊断,输出为对应故障类型的概率,并将该方法与小波神经网络和支持向量机进行对比。结果表明,所提方法正确率最高,并能分析问题的不确定性,具有诊断多重故障的能力。In order to improve the accuracy of transformer fault diagnosis,a hybrid model which combines the FRVM with the depth belief network(DBN)was proposed.The method established the relationship between gas and fault types using the ration of dissolved gas as input parameter.Considering that DBN needed to extract huge amount of feature information,the FRVM was used to separate the discharge and overheating faults to reduce the feature information that DBN needed to extract.Then DBN was used to realize further fault diagnosis.The output was the probability of the corresponding fault types.The proposed method was compared with wavelet neural networks and SVM.The results show that the proposed method has the highest accuracy,and can analyze the uncertainty of the problem,as well as has the ability to diagnose multiple faults.

关 键 词：变压器 油中溶解气体分析 故障诊断 快速相关向量机 深度信念网络 

分 类 号：TM71[电气工程—电力系统及自动化]