Performance Analysis of Faults Detection in Wind Turbine Generator Based on High-Resolution Frequency Estimation Methods

Saad Chakkor 1, * Mostafa Baghouri 1 Abderrahmane Hajraoui 1
* Auteur correspondant
1 Communication and Detection Systems Laboratory
Communication and Detection Systems Laboratory
Abstract : Electrical energy production based on wind power has become the most popular renewable resources in the recent years because it gets reliable clean energy with minimum cost. The major challenge for wind turbines is the electrical and the mechanical failures which can occur at any time causing prospective breakdowns and damages and therefore it leads to machine downtimes and to energy production loss. To circumvent this problem, several tools and techniques have been developed and used to enhance fault detection and diagnosis to be found in the stator current signature for wind turbines generators. Among these methods, parametric or super-resolution frequency estimation methods, which provides typical spectrum estimation, can be useful for this purpose. Facing on the plurality of these algorithms, a comparative performance analysis is made to evaluate robustness based on different metrics: accuracy, dispersion, computation cost, perturbations and faults severity. Finally, simulation results in MATLAB with most occurring faults indicate that ESPRIT and R-MUSIC algorithms have high capability of correctly identifying the frequencies of fault characteristic components, a performance ranking had been carried out to demonstrate the efficiency of the studied methods in faults detecting.
Liste complète des métadonnées

Littérature citée [37 références]  Voir  Masquer  Télécharger
Contributeur : Saad Chakkor <>
Soumis le : lundi 22 septembre 2014 - 14:17:09
Dernière modification le : jeudi 2 août 2018 - 16:02:01
Document(s) archivé(s) le : mardi 23 décembre 2014 - 10:31:21


Fichiers éditeurs autorisés sur une archive ouverte



Saad Chakkor, Mostafa Baghouri, Abderrahmane Hajraoui. Performance Analysis of Faults Detection in Wind Turbine Generator Based on High-Resolution Frequency Estimation Methods. International Journal of Advanced Computer Science and Applications IJACSA, SAI Publisher, 2014, May 2014, 5 (4), pp.139-148. 〈〉. 〈10.14569/IJACSA.2014.050420〉. 〈hal-01066451〉



Consultations de la notice


Téléchargements de fichiers