Online Sequential Extreme Learning Machine for Partial Discharge Pattern Recognition of Transformer
| Autor: | Hui Song, Ge-Hao Sheng, Qinqin Zhang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
010302 applied physics
Online sequential Computer science business.industry Pattern recognition 02 engineering and technology 01 natural sciences Support vector machine Back propagation neural network Ultra high frequency 0103 physical sciences Partial discharge 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Extreme learning machine |
| Zdroj: | 2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D). |
| DOI: | 10.1109/tdc.2018.8440451 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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