Wind generator fault detection using end effects processing of Hilbert-Huang transform

Author

Po-Hung Chen ; Deng-Fa Lin ; Ming-Ciiang Tsai ; Li-Ming Chen ; An Liu

Author_Institution

Dept. of Electr. Eng., St. John´s Univ., Taipei, Taiwan

Volume

2

fYear

2014

fDate

13-16 July 2014

Firstpage

615

Lastpage

620

Abstract

This paper presents a novel approach to improve the end effects of Hilbert-Huang transform (HHT) for the wind generator fault detection. The proposed approach utilizes a back-propagation neural network (BPNN) to extent the end of the spectrum. HHT consists of empirical mode decomposition (EMD) and Hilbert transform (HT), on which the end effects distort Hilbert spectrum. The extension of the two ends obtained by the BPNN forms a new spectrum to improve the end effects. Experimental results indicate utilizing the proposed approach to analyze generator currents can improve the exactitude of Hilbert spectrum.

Keywords

Hilbert transforms; backpropagation; fault diagnosis; neural nets; power engineering computing; power generation faults; wind turbines; BPNN; Hilbert spectrum; Hilbert-Huang transform; back-propagation neural network; end effects processing; generator currents; wind generator fault detection; Abstracts; Back-propagation neural network; Empirical mode decomposition; End effect; Hilbert-Huang transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2014 International Conference on

Conference_Location

Lanzhou

ISSN

2160-133X

Print_ISBN

978-1-4799-4216-9

Type

conf

DOI

10.1109/ICMLC.2014.7009679

Filename

7009679