Drive train system dynamic analysis; Application to wind turbines

Licentiate thesis, 2016

To facilitate the design and production of highly efficient and reliable wind turbine drive trains, the project deals with the mathematical modelling and experimental study of drive train system dynamics. A typical drive train is considered as the subsystem of a wind turbine that transfers mechanical power from the rotor hub to the generator, and thereby plays an important role in the system dynamics and 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 and optimum structural performance. The current work contributes to enhanced knowledge in this field with focus on interaction between functional components and system dynamic response, faults modelling and detectability of defects in bearings in wind turbine drive trains. The overall performance of a drive train can be evaluated from different perspectives. In this thesis, the dynamics behaviour of the high speed shaft drive train is evaluated by proposed objective functions referring to displacements, loads, and frequency responses. To have a better insight into wind turbine dynamics, the global sensitivity analysis (GSA) of high speed shaft drive train dynamics with respect to input structural parameters is considered. The multiplicative dimension reduction method is employed to provide the mapping between the objective functions' sensitivity indices and design variables. The results of such analysis can narrow down the number of input variables for design problem and improve the computational efficiency. The proposed GSA methodology is applied for the system modelled analysis of high speed shaft subsystem of a drive train. Moreover, by introducing defects in functional components and investigating sensitivity indices, detectability of faults by GSA is proved. The results show that the proposed methodology is capable of detecting damage in the functional components such as bearings in early stage before a complete failure. The application of this methodology within the detection, prediction, and prevention framework has a potential to reduce the maintenance cost for critical components. The results can also provide a better understanding and useful hints in wind turbine drive train system dynamics with respect to different structural parameters, ultimately designing more efficient drive trains.