Hilbert Transform-Based Bearing Failure Detection in DFIG-Based Wind Turbines

Yassine Amirat (1) , Vincent V. Choqueuse (1) , Mohamed Benbouzid (1) , Sylvie Turri (1)

1 LBMS - Laboratoire brestois de mécanique et des systèmes

Cost-effective, predictive and proactive maintenance of wind turbines assumes more importance with the increasing number of installed wind farms in more remote location (offshore). A well-known method for assessing impeding problems is to use current sensors installed within the wind turbine generator. This paper describes then an approach based on the generator stator current data collection and attempts to highlight the use of the Hilbert transform for failure detection in a doubly-fed induction generator-based. Indeed, this generator is commonly used in modern variable-speed wind turbines. The proposed failure detection technique has been validated experimentally regarding bearing failures. Indeed, a large fraction of wind turbine downtime is due to bearing failures, particularly in the generator and gearbox.

Mots clés: Wind turbine; doubly-fed induction generator; fault detection; amplitude modulation; Hilbert transform

Domaines: Energie électrique

Yassine Amirat, Vincent V. Choqueuse, Mohamed Benbouzid, Sylvie Turri. Hilbert Transform-Based Bearing Failure Detection in DFIG-Based Wind Turbines. International Review of Electrical Engineering, 2011, 6 (3), pp.1249-1256. ⟨hal-00630431⟩