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Power Electrical Systems

Extended Papers 2017

Ed. by Derbel, Faouzi / Derbel, Nabil / Kanoun, Olfa

Series:Advances in Systems, Signals and Devices 7

eBook (PDF)
Publication Date:
July 2018
Copyright year:
2018
ISBN
978-3-11-047052-9
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A NewWavelet−ANN Approach Based on Feature Extraction for a FAST Wind Turbine Model Diagnosis System

Bakir, T. / Boumedyen, B. / Odghaard, P.F. / Abdelkrim, M.N. / Aubrun, C.

Abstract

This work presents a method to increased the fault detection accuracy in wind turbine system using a combination of Wavelet, Principal Component Analysis (PCA), Parseval’s theorem and Neural Networks. Preprocessing, feature extraction and classification rules are three crucial issues for fault detection. To employ this issues, a two stages hybrid approach is used. The first stage is composed of preprocessing and feature extraction steps, where wavelet transform is exploited for preprocessing residual signals while two tools are used and compared to extract features based on PCA and Parseval’s theorem. During the classification stage, the Artificial Neural Network (ANN) is explored to achieve a robust decision in presence and absence of faults. The approaches are applied on a FAST wind turbine system

Citation Information

T. Bakir, B. Boumedyen, P.F. Odghaard, M.N. Abdelkrim, C. Aubrun (2018). A NewWavelet−ANN Approach Based on Feature Extraction for a FAST Wind Turbine Model Diagnosis System. In Faouzi Derbel, Nabil Derbel, Olfa Kanoun (Eds.), Power Electrical Systems: Extended Papers 2017 (pp. 155–178). Berlin, Boston: De Gruyter. https://doi.org/10.1515/9783110470529-010

Book DOI: https://doi.org/10.1515/9783110470529

Online ISBN: 9783110470529

© 2018 Walter de Gruyter GmbH, Berlin/Munich/BostonGet Permission

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