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Conference programme 

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Poster session

Lead Session Chair:
Stephan Barth, Managing Director, ForWind - Center for Wind Energy Research, Germany
Bachir Batoun Unité de Recherche Appliquée en Energies Renouvelables, Algeria
Co-authors:
Bachir BATOUN (1) F P
(1) Unité de recherche appliquée en énergies renouvelables, Ghardaia, Algeria

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Poster
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Abstract

Diagnostisis and parametrs estimation of wind energy generator

Introduction

In this paper, the authors propose a methodology of diagnostisis aund parameters estimation of a wind energy generator using wavelets transform. This method consists in applying wavelet transform to stator and rotor currents in order to identify and localize any occurring fault. After presentation of characteristics of wavelet transform, authors choose the algorithm of Morley, Gabor and Wigner-Ville to classify different faults affecting the stator and rotor currents.

Approach

Over the last two decades, diagnostic has become increasingly important for improving system reliability [1]. The risk of wind turbine generator failure can be remarkably reduced if regular maintenance is operated. Moreover over the last decade, wavelet analysis has been applied in different areas, such as image processing and geosciences, as an alternative to Fourier analysis [2] and [3]. Renewable energies contributed to an increase by 157531 MW to world energy budget in 2009. The growth rate of production between 2008 and 2009 was about 30% [4] and it is important to ensure a considerable electricity production.

Main body of abstract

In this work fault signature on current wind turbine generator has been demonstrated and clarified both theoretically and empirically.
Whatever time–frequency distribution the faulty generator would change packet nature through prior choice of function analysis.
Implementing wavelet distribution of motor oscillating signal may give important results regarding the surveillance of wind generator.
We have presented a method of error detection for dynamic linear systems; it is used in tandem with another procedure based on the polynomial representation. This method is applied to a Doubly Fed Induction Generator (DFIG) of a wind turbine by using a simulator. Further more, the results also show that the proposed fault diagnosis methods can easily recognize the fault category and indicate the possibilities of others. A particular case of nonlinear systems was studied; it corresponds to a wind turbine operating at variable speed.


Conclusion

We have presented a method of error detection for dynamic linear systems; it is used in tandem with another procedure based on the polynomial representation. This method is applied to a Doubly Fed Induction Generator (DFIG) of a wind turbine by using a simulator. Further more, the results also show that the proposed fault diagnosis methods can easily recognize the fault category and indicate the possibilities of others. A particular case of nonlinear systems was studied; it corresponds to a wind turbine operating at variable speed.



Learning objectives
Our next work will focus on other identification systems with the possibility to estimate the unmeasured variables.