NDLI: Application of a combinatorial neural network model based on cluster analysis in transformer fault diagnosis

Content Provider	IEEE Xplore Digital Library
Author	Liu Na Gao Wensheng Tan Kexiong Wang Xiaoning
Copyright Year	2002
Description	Author affiliation: Dept. of Electr. Eng., Tsinghua Univ., Beijing, China (Liu Na; Gao Wensheng; Tan Kexiong; Wang Xiaoning)
Abstract	The multi-resolution identification of transformer faults is significant for the maintenance of transformer. In this paper, a combinatorial artificial neural network (ANN) model, based on cluster analysis of data of dissolved gases in transformer oil, is presented. A more detailed classification is necessary to obtain explicit diagnosis results. Based on the discussion of traditional classification methods, a twelve-fault classification method is established. However there are similarities among these faults. which should be considered before constructing the combinatorial model. Hence, hierachical cluster analysis is chosen to investigate the similarities and helps to construct the model. Finally, the application results show the value of this model for the diagnosis of transformer faults.
Sponsorship	IEEE Region 10 IEEE Comput. Soc. Beijing Chapter IEEE Commun. Soc. Beijing Chapter IEEE Control Syst. Soc. Beijing Chapter IEEE Power Eng. Soc. Beijing Chapter IEEE Power Electron. Soc. Beijing Chapter IEEE Signal Process. Soc. Biejing Chapter
Starting Page	1873
Ending Page	1876
File Size	235621
Page Count	4
File Format	PDF
ISBN	0780374908
DOI	10.1109/TENCON.2002.1182702
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-10-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Intelligent networks Fault diagnosis Power transformers Dissolved gas analysis Oil insulation Artificial neural networks Shape measurement Data analysis Gases
Content Type	Text
Resource Type	Article

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