Transformer fault diagnosis using continuous sparse autoencoder

Lukun Wang; Xiaoying Zhao; Jiangnan Pei; Gongyou Tang

doi:10.1186/s40064-016-2107-7

Transformer fault diagnosis using continuous sparse autoencoder

Springerplus. 2016 Apr 14:5:448. doi: 10.1186/s40064-016-2107-7. eCollection 2016.

Authors

Lukun Wang¹, Xiaoying Zhao², Jiangnan Pei³, Gongyou Tang¹

Affiliations

¹ College of Information Science and Engineering, Ocean University of China, Qingdao, China.
² College of Foreign Languages, Taishan Medical University, Taian, China.
³ College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China.

Abstract

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.

Keywords: Continuous sparse autoencoder; Deep belief network; Deep learning; Dissolved gas analysis; Transformer fault.