Wind Turbine Drive Train System Dynamics ; Multibody Dynamic Modelling and Global Sensitivity Analysis
Doctoral thesis, 2018
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.
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.
Global sensitivity analysis
Model validation
Multibody dynamics system mod-elling
Gearbox modelling
Kinematic constraints
Bearing defects detection
Wind turbine drive train dynamics
Floating reference frame
EA, lecture hall, EDIT trappa C, D och H, EDIT
Opponent: Professor Jussi Sopanen, Dept. of Mechanical Engineering, Lappeenranta University of Technology, Finland
Author
Saeed Asadi
Chalmers, Mechanics and Maritime Sciences (M2), Dynamics
Wind energy is nowadays among the fastest-growing renewable energy sources, and important in the transition towards a fossil free energy production system. Nevertheless, modern wind turbines are subjected to different kinds of failures in drive train components, such as main bearings, gearboxes, and generators.
Downtime loss and unreliable operation and maintenance affects drastically on the cost of energy and the economic benefit of wind power. Thus, to ensure an economic and sustainable future of the wind power industry, one specific goal is to reduce drive train failures. This could be achieved by developing more efficient and realistic mathematical models for drive trains functional components, estimate the risk for different types of damages, and consequently, assess the drive train fatigue life.
The present work contributes to mathematical modeling of direct and indirect drive train wind turbine.
The analysis has been carried out to quantify the effects of structural parameters as well as excitation parameters representing the wind load components on a set of objective functions quantifying the drive train dynamic response. To this end, so-called Global Sensitivity Analysis (GSA) of the dynamic response of the system is conducted under different wind conditions such as different mean wind speeds, turbulence and vertical inclination of incoming wind. The studied objective functions representing the drive train dynamic response are the front and rear bearing damage indices and the radial deflections at the rotor hub and the generator.
The outcome of this research can be used to estimate how uncertainties in structural and excitation parameters may affect the evaluation of drive train dynamics which is crucial in the design of robust drive train systems. The result can also be used to narrow down the number of input variables for design problems which leads to improved computational efficiency in optimization problems, design of structural health monitoring, and gain a better understanding of the drive train dynamics.
Downtime loss and unreliable operation and maintenance affects drastically on the cost of energy and the economic benefit of wind power. Thus, to ensure an economic and sustainable future of the wind power industry, one specific goal is to reduce drive train failures. This could be achieved by developing more efficient and realistic mathematical models for drive trains functional components, estimate the risk for different types of damages, and consequently, assess the drive train fatigue life.
The present work contributes to mathematical modeling of direct and indirect drive train wind turbine.
The analysis has been carried out to quantify the effects of structural parameters as well as excitation parameters representing the wind load components on a set of objective functions quantifying the drive train dynamic response. To this end, so-called Global Sensitivity Analysis (GSA) of the dynamic response of the system is conducted under different wind conditions such as different mean wind speeds, turbulence and vertical inclination of incoming wind. The studied objective functions representing the drive train dynamic response are the front and rear bearing damage indices and the radial deflections at the rotor hub and the generator.
The outcome of this research can be used to estimate how uncertainties in structural and excitation parameters may affect the evaluation of drive train dynamics which is crucial in the design of robust drive train systems. The result can also be used to narrow down the number of input variables for design problems which leads to improved computational efficiency in optimization problems, design of structural health monitoring, and gain a better understanding of the drive train dynamics.
Subject Categories
Applied Mechanics
Energy Engineering
ISBN
978-91-7597-747-8
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4428
Publisher
Chalmers
EA, lecture hall, EDIT trappa C, D och H, EDIT
Opponent: Professor Jussi Sopanen, Dept. of Mechanical Engineering, Lappeenranta University of Technology, Finland