Fault diagnosis of a transformer based on polynomial neural networks-Reference-Cited by-同舟云学术

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Fault diagnosis of a transformer based on polynomial neural networks

Published:2017-07-10 Issue:S4 Volume:22 Page:9941-9949
ISSN:1386-7857
Container-title:Cluster Computing
language:en
Short-container-title:Cluster Comput

Author:

Zou Ajin,Deng Rui,Mei Qixiang,Zou Lang

Funder

National Natural Science Foundation of China

“Climbing Plan” Special Fund Project of Guangdong Province

Science and Technology Project of Guangdong Province

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Link

http://link.springer.com/content/pdf/10.1007/s10586-017-1020-3.pdf

Reference44 articles.

1. Pandya, A.A., Parekh, B.R.: Interpretation of sweep frequency response analysis (SFRA) traces for the open circuit and short circuit winding fault damages of the power transformer. Int. J. Electr. Power Energy Syst. 62(11), 890–896 (2014)

2. Souahlia, S., Bacha, K., Chaari, A.: MLP neural network-based decision for power transformers fault diagnosis using an improved combination of Rogers and Doernenburg ratios DGA. Int. J. Electr. Power Energy Syst. 43(1), 1346–1353 (2012)

3. Sun, Y.J., Zhang, S., Miao, C.X., et al.: Improved BP neural network for transformer fault diagnosis. J. China Univ. Min. Technol. 17(1), 138–142 (2007)

4. Shao, K.: A novel method of transformer fault diagnosis based on extension theory and information fusion in wireless sensor networks. Energy Procedia 12(12), 669–678 (2011)

5. Han, H., Wang, H.J., Dong, X.C.: Transformer fault Diagnosis based on feature selection and parameter optimization. Energy Procedia 12(39), 662–668 (2011)

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