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
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.