Wind Turbine Drive Train System Dynamics ; Multibody Dynamic Modelling and Global Sensitivity Analysis

To facilitate the design and production of highly efficient and reliable wind turbine drive trains, this thesis deals with the mathematical modelling and analysis of drive train system dynamics. The drive train is considered as the subsystem of the wind turbine that transfers mechanical power from the rotor hub to the generator, and thereby plays an important role in the system dynamics and the efficiency of wind turbine operation.The dynamics of wind turbines is complex and a critical area of study for the wind industry. The multidisciplinary nature of wind turbine design adds to the complexity of this task, as the subsystems of a wind turbine need to be tuned with respect to a common objective to achieve a cost effective, reliable and optimum structural and dynamic performance.The overall performance of a drive train can be evaluated from different perspectives. In this thesis, mathematical model of drive train wind turbine for both direct and indirect drive train has been developed based on multibody dynamic modelling formalism. Afterwards, the dynamics behaviour of the drive train is evaluated by proposed objective functions referring to displacements, loads, fatigue damage indicators, and frequency responses. These objective functions are investigated for several wind operational scenarios such as normal operation, turbulent, vertical inclination cases.The work also contributes to enhanced knowledge in the field with focus on the inter-action between functional components and system dynamic response, faults modelling and detectability of defects in functional components such as bearings, and couplings in wind turbine drive trains. To have a better insight into wind turbine dynamics, the global sensitivity analysis (GSA) of the objective functions with respect to input structural parameters is considered. By introducing defects in functional components and investi-gating sensitivity indices, detectability of faults is proved. GSA also demonstrates the most influential input parameters to the output objective functions. The results of such analysis not only can narrow down the number of input variables for design problems, but also give understanding on which structural parameters are most important to have pre-cise data for, ultimately designing more efficient drive trains in terms of cost and durability