Advanced Signal Processing Techniques for Fault Detection and Diagnosis in a Wind Turbine Induction Generator Drive Train: A Comparative Study

E. El Ahmar (1) , Vincent V. Choqueuse (2) , Mohamed Benbouzid (2) , Yassine Amirat (2) , Joseph El Assad (1) , R. Karam (3) , Said Farah (3)

1 University of kaslik
2 LBMS - Laboratoire brestois de mécanique et des systèmes
3 University of kaslik

This paper deals with the diagnosis of Wind Turbines based on generator current analysis. It provides a comparative study between traditional signal processing methods, such as periodograms, with more sophisticated approaches. Performances of these techniques are assessed through simulation experiments and compared for several types of fault, including air-gap eccentricities, broken rotor bars and bearing damages.

Mots clés: Wind turbines; motor current signature analysis; time-frequency signal processing methods; wavelet analysis; failure diagnosis

Domaines: Energie électrique; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

E. El Ahmar, Vincent V. Choqueuse, Mohamed Benbouzid, Yassine Amirat, Joseph El Assad, et al.. Advanced Signal Processing Techniques for Fault Detection and Diagnosis in a Wind Turbine Induction Generator Drive Train: A Comparative Study. IEEE Energy Conversion Congress and Exposition (ECCE), 2010, Sep 2010, Atlanta, United States. pp.3576 - 3581, ⟨10.1109/ECCE.2010.5617914⟩. ⟨hal-00532625⟩