Power Transformer Fault Diagnosis System Based on Learn++

Article Preview

Abstract:

Aiming at the shortages of traditional method for power transformer fault diagnosis, the ensemble idea and incremental learning idea are used for better performance. The SVM is selected to establish the fault diagnosis models as sub learning machines. And then, the Learn++ algorithm is used to aggregate the sub learning machines. The new with new method will ensure the accuracy of fault diagnosis, and will update online. The experiments demonstrate that the performance of power transformer fault diagnosis system based on Learn++ is the best.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

2053-2056

Citation:

Online since:

August 2014

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] IEEE, IEEE Guide for the Interpretation of Gases Generated in Oil Immersed Transformers, standard ed., Picataway, NJ: IEEE Press, (1992).

DOI: 10.1109/ieeestd.2009.4776518

Google Scholar

[2] Duan H D, Yao X. Power Transformers Fault Diagnosis Based on Fuzzy-RBF Neural Network[J]. Advanced Materials Research, 2013, 614: 1303-1306.

DOI: 10.4028/www.scientific.net/amr.614-615.1303

Google Scholar

[3] Zu W, Yuan J, Zhang W. Determining method for reliability distribution function of transformer fault diagnosis based on SVM[J]. Heilongjiang Dianli Jishu(Heilongjiang Electric Power), 2013, 35(2).

Google Scholar

[4] Vapnik, V. N. (1999). The nature of statistical learning theory. New York: Springer.

Google Scholar

[5] Mayoraz, E., & Alpaydin, E. (1999). Support vector machines for multi-class classification. Proceedings of International Workshop on Artificial Neural Networks, 2, 833–842.

DOI: 10.1007/bfb0100551

Google Scholar

[6] Patel A J, Patel J S. Ensemble systems and incremental learning[C]/Intelligent Systems and Signal Processing (ISSP), 2013 International Conference on. IEEE, 2013: 365-368.

DOI: 10.1109/issp.2013.6526936

Google Scholar

[7] R. Polikar, L. Udpa, S. Udpa , Learn++ : an incremental learning algorithm for supervised neural networks [J] . IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 2001, 31( 4) : 497-508.

DOI: 10.1109/5326.983933

Google Scholar