标题：Transformer fault diagnosis using continuous sparse autoencoder
作者：Wang, Lukun; Zhao, Xiaoying; Pei, Jiangnan; Tang, Gongyou
作者机构：[Wang, Lukun; Tang, Gongyou] Ocean Univ China, Coll Informat Sci & Engn, Qingdao, Peoples R China.; [Zhao, Xiaoying] Taishan Med Univ, Coll Foreign 更多
通讯作者地址：[Wang, LK]Ocean Univ China, Coll Informat Sci & Engn, Qingdao, Peoples R China.
关键词：Continuous sparse autoencoder; Dissolved gas analysis; Deep belief; network; Deep learning; Transformer fault
摘要：This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the concentrations of dissolved gases. Then IEC three ratios data is normalized to reduce data singularity and improve training speed. Secondly, deep belief network is established by two layers of CSAE and one layer of back propagation (BP) network. Thirdly, CSAE is adopted to unsupervised training and getting features. Then BP network is used for supervised training and getting transformer fault. Finally, the experimental data from IEC TC 10 dataset aims to illustrate the effectiveness of the presented approach. Comparative experiments clearly show that CSAE can extract features from the original data, and achieve a superior correct differentiation rate on transformer fault diagnosis.