引用本文: | 石 鑫,朱永利,宁晓光,王刘旺,孙 岗,陈国强.基于深度自编码网络的电力变压器故障诊断[J].电力自动化设备,2016,36(5): |
| SHI Xin,ZHU Yongli,NING Xiaoguang,WANG Liuwang,SUN Gang,CHEN Guoqiang.Transformer fault diagnosis based on deep auto-encoder network[J].Electric Power Automation Equipment,2016,36(5): |
|
摘要: |
基于深度自编码网络(DAEN),构建了分类深度自编码网络(CDAEN)模型。结合电力变压器在线监测油中溶解气体分析(DGA)数据,提出了基于CDAEN的变压器故障诊断方法。所提方法利用大量无标签样本进行预训练,优化模型参数,并利用少量有标签样本进行微调。实例分析表明,与基于反向传播神经网络(BPNN)、支持向量机(SVM)的故障诊断方法相比,所提方法的诊断正确率更高。 |
关键词: 深度自编码网络 电力变压器 故障诊断 油中溶解气体分析 反向传播神经网络 支持向量机 |
DOI: |
分类号: |
基金项目: |
|
Transformer fault diagnosis based on deep auto-encoder network |
SHI Xin1, ZHU Yongli1, NING Xiaoguang1, WANG Liuwang1, SUN Gang2, CHEN Guoqiang2
|
1.School of Control and Computer Engineering,North China Electric Power University,Baoding 071003,China;2.State Grid Corporation of China,Beijing 100031,China
|
Abstract: |
A CDAEN(Classified Deep Auto-Encoder Network) model is built based on the DAEN(Deep Auto-Encoder Network). Combined with the on-line monitored data of DGA(Dissolved Gas-in-oil Analysis) for power transformer,a method of transformer fault diagnosis based on CDEAN is proposed,which optimizes the parameters of CDAEN model by the pre-training with massive unlabeled samples and adjusts them with a few labeled samples. Results of case analysis show that the proposed method has higher diagnosis accuracy than those based on the BPNN(Back Propagation Neural Network) and the SVM(Support Vector Machine). |
Key words: deep auto-encoder network power transformers fault diagnosis dissolved gas-in-oil analysis back propagation neural network support vector machines |