The traditional partial discharge（PD） pattern recognition algorithms have low recognition accuracies and slow recognition speed in practical engineering applications because of their limitations

including a large number of parameters to tune

the difficulty in optimizing parameters and low learning rates.Therefore

we proposed a PD pattern recognition method for transformer based on the Online Sequential-Extreme Learning Machine（OS-ELM） algorithm.OS-ELM is an online-learning and improved algorithm of Extreme Learning Machine（ELM）

and a new type of Single-hidden Layer Feed-forward neural network（SLFN）.Meanwhile

a lot of experimental data were obtained from real transformer in the high voltage laboratory on the PD experiments based on Ultra High Frequency（UHF） detection method.In addition

OS-ELM is analyzed and compared with ELM

Support Vector Machine（SVM） and BP neural network（BPNN） in both pattern recognition effect and performance aspects.The results show that the accuracy of OS-ELM is 5.2% and 23.2% higher than that of SVM and BPNN

respectively.When reducing the size of the training samples

the fluctuation of OS-ELM recognition results is significantly smaller than that of SVM and BPNN

showing better generalization ability.Besides

the training time of OS-ELM is only 0.031 2 s

far less than that of SVM and BPNN.Therefore

OS-ELM is more suitable for engineering applications of large data volume samples.