Optimizing Online Sequential Extreme Learning Machine Parameters and Application to Transformer Fault Diagnosis

DOI

10.2991/icmmcce-15.2015.177How to use a DOI?

Keywords

Online Sequential;extreme learning machine;Genetic Algorithm Optimization;powers transformer fault diagnosis;parameter optimization

Abstract

In order to solve the problem that the (OS-ELM) is used in the fault diagnosis of the transformer, the genetic algorithm (Algorithm Genetic) is applied to the on-line extreme learning machine, and a new method of transformer fault diagnosis is proposed. In this method, the number of hidden layer neurons of the Block L, the data set size N, and the hidden layer activation function are selected by the Algorithm Genetic optimization algorithm. Through simulation test, the fault diagnosis of transformer is 99.56%, and the test time is 0.0024 s. Compared with the optimization, the diagnostic accuracy and the test time of the transformer fault are improved obviously.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Wenrong Kang
AU  - Wenyan Chen
PY  - 2015/12
DA  - 2015/12
TI  - Optimizing Online Sequential Extreme Learning Machine Parameters and Application to Transformer Fault Diagnosis
BT  - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015
PB  - Atlantis Press
SP  - 1299
EP  - 1304
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmcce-15.2015.177
DO  - 10.2991/icmmcce-15.2015.177
ID  - Kang2015/12
ER  -