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Research on Transformer Fault Diagnosis Based on Online Sequential Extreme Learning Machine

Author(s):

Yuancheng Li, Xiaohan Wang* and Yingying Zhang   Pages 1 - 6 ( 6 )

Abstract:


Background: Transformer is one of the most important pivot equipment in an electric system which undertakes major responsibility. Therefore, it is very important to identify the fault of the transformer accurately and transformer fault diagnosis technology becomes one topic with great research value. Methods: In this paper, after analyzing the shortcomings of traditional methods, we have proposed a transformer fault diagnosis method based on online sequential extreme learning machine (OS-ELM) and dissolved gas-in-oil analysis. This method has better precision than some commonly used methods at present. Furthermore, OS-ELM is more efficient than ELM. In addition, we analyze the effect of different parameter selection on the performance of the model by contrast experiments. Result: The experimental result shows that OS-ELM has certain promotion in precision than some traditional methods and can obviously improve the speed of training than ELM. Besides, it is known that the number of neurons in the hidden layer and the size of dataset have a great effect on the model. Conclusion: The transformer fault diagnosis method based on OS-ELM can effectively identify the faults and more efficient than ELM.

Keywords:

Dissolved gas-in-oil analysis, extreme learning machine, fault diagnosis, machine learning, online sequential learning, power transformer.

Affiliation:

North China Electric Power University, School of Control and Computer Engineering, Beinong Road, Huilongguan Town, Changping District, Beijing, North China Electric Power University, School of Control and Computer Engineering, Beinong Road, Huilongguan Town, Changping District, Beijing, North China Electric Power University, School of Control and Computer Engineering, Beinong Road, Huilongguan Town, Changping District, Beijing



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