Modal Parameter Estimation for Operational Wind Turbines

Emilio Di Lorenzo 1 Simone Manzato 1 Bart Peeters 1 Francesco Marulo 2
1 LMS - LMS International [Leuven]
2 Università degli studi di Napoli Federico II
Abstract : Wind turbines are time-varying systems excited by loads due to the wind and to the interaction between blades, tower and drivetrain. Since it is very difficult to measure the loads, the modal identification procedure needs to rely only on the output measurement data. Operational Modal Analysis (OMA) is well suited for the estimation of modal parameters in several cases. One of the main conditions needed for its application is the linear time-invariance of the system. It is the case of parked wind turbines, but the requirement is violated in the case of operating wind turbines. Therefore, OMA technique needs to be adapted in order to be applied to linear time-variant systems. Alternatively, time-variant systems should be converted to time-invariant ones before applying the classical OMA. Multi-Blade Coordinate transformation (MBC) allows having information on the dynamic interaction between the nonrotating components and the rotor. The time periodic system is converted into a time invariant one. Conventional OMA technique can then be applied to estimate the modal parameters. First of all a multibody model of a wind turbine is considered and some assessments on how to combine numerical and experimental techniques for Structural Health Monitoring (SHM) of operating wind turbines are investigated.
Keywords : Parametric identification Energy SHM applications for wind turbines Modal analysis
Type de document :
Communication dans un congrès
Le Cam, Vincent and Mevel, Laurent and Schoefs, Franck. EWSHM - 7th European Workshop on Structural Health Monitoring, Jul 2014, Nantes, France. 2014
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Emilio Di Lorenzo, Simone Manzato, Bart Peeters, Francesco Marulo. Modal Parameter Estimation for Operational Wind Turbines. Le Cam, Vincent and Mevel, Laurent and Schoefs, Franck. EWSHM - 7th European Workshop on Structural Health Monitoring, Jul 2014, Nantes, France. 2014. 〈hal-01020443〉

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