许倩文,吉兴全,张玉振,李军,于永进.栈式降噪自编码网络在变压器故障诊断中的应用[J].电测与仪表,2018,55(17):62-67. xuqianwen,jixingquan,zhangyuzhen,lijun,yuyongjin.Application of stacked denoising auto-encoder network in fault diagnosis of power transformer[J].Electrical Measurement & Instrumentation,2018,55(17):62-67.
栈式降噪自编码网络在变压器故障诊断中的应用
Application of stacked denoising auto-encoder network in fault diagnosis of power transformer
The three-ratio method and Rogers method, which are widely used in the research of transformer fault diagnosis, have the disadvantage of incomplete coding and too absolute judgment criteria.To solve this problem,applied the deep neural network to fault diagnosis of power transformer,and proposed a fault diagnosis method based on the stacked denoising auto-encoder.A deep network model is established, and a self-adaptive and unsupervised pre-training is implemented by greedy coding. To achieve adaptive extraction and mining of high-dimensional deep fault features, the back-propagation algorithm is used to fine tune the model.Finally, the Softmax classifier is used to classify the faults.The experimental results show that compared with the traditional Back propagation (BP) neural network, the proposed deep neural network can solve the problem of easy to fall into the local minimization and slow convergence of the traditional BP neural network, and diagnosis the transformer faultmore effectively.
