Online Sequential Extreme Learning Machine for Partial Discharge Pattern Recognition of Transformer
Qinqin Zhang, Hui Song, Gehao Sheng · 2018
Traditional pattern recognition algorithms have limitations including slow training speed and low recognition accuracy in practical engineering applications. In this paper, a new method based on Online Sequential Extreme Learning Machine (OS-ELM) is proposed. Data samples have been obtained from PD experiment of real transformer based on Ultra High Frequency (UHF) detection method. In addition, OS-ELM is compared with Extreme Learning Machine (ELM), Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) in both recognition accuracy and performance aspects. The results show that OS-ELM is not only much faster in learning speed, but also more excellent in recognition accuracy, thus more suitable for engineering applications with large volume of data samples.