Research on Intelligent Diagnosis Technology of Transformer Fault

Hong Qi Wu; Xiao Bin Li

doi:10.4028/www.scientific.net/AMM.385-386.589

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 385-386Research on Intelligent Diagnosis Technology of...

Research on Intelligent Diagnosis Technology of Transformer Fault

Abstract:

In order to improve the diagnosis rates of transformer fault, a research on application of RBF neural network is carried out. The structure and working principle of radial basis function (RBF) neural network are analyzed and a three layer RBF network is also designed for transformer fault diagnosis. It is proved by MATLAB experiment that RBF neural network is a strong classifier which is used to diagnose transformer fault effectively.

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Periodical:

Applied Mechanics and Materials (Volumes 385-386)

Pages:

589-592

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.385-386.589

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Online since:

August 2013

Authors:

Hong Qi Wu, Xiao Bin Li

Keywords:

Fault Diagnosis, RBF Neural Network, Transformer

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References

[1] Zhu kai Wang Zhenglin MATLAB Neural network [M] beijing: Electronics Industry Press2009. 9.

Google Scholar

[2] Xia Hong, Liu Yongkuo, Xie Chunli, The Fault Diagnosis Technology of Equipment[M] Harbin Institute of Technology Press.

Google Scholar

[3] Sun XinCai Present situation and development of condition on-line monitoring and diagnosis technology for power transmission and transformation equipment[J] Electric Power, 2005. 2, P56-58.

Google Scholar

[4] Zhang Guoyun Zhang jing Design for On-Line Monitoring System of Dissolved Gasses in Oil Based on Artificial Nervous Network[J] Transformer 2003. 3 P28-31.

Google Scholar

[5] John Platt. A Resoure-Allocating network for Function Inte rpolation[J]. eural Computation, 2002, 3(5): P213-255.

Google Scholar

[6] Nobuyuki Matsui, Masato Takai, Haruhiko Nishimura. Neural network based on QBP and its performance, IEEE Internationnal Workshop on Robot and Human Interactive Communication Osaka, Japan, (2000).

Google Scholar

[7] Barry H Ward. stan Lindgren. A survey of develop ments in insulation Monitoring of power transformers[c]. IEEE International Symposium on Electrical. 2006: 321-325.

Google Scholar

[8] Chang Wenping, Fan Zhen, Wang Xiaomin. On-line Monitoring System Of Transformer Fault and Its Application. Control & Automation. 2005（12）: 125-126.

Google Scholar