Wind turbines play a major role in the transformation from a fossil fuel based
energy production to a more sustainable production of energy. Total-cost-of-ownership
is an important parameter when investors decide in which energy
technology they should place their capital. Modern wind turbines are controlled
by pitching the blades and by controlling the electro-magnetic torque
of the generator, thus slowing the rotation of the blades. Improved control of
wind turbines, leading to reduced fatigue loads, can be exploited by using less
materials in the construction of the wind turbine or by reducing the need for
maintenance of the wind turbine. Either way, better total-cost-of-ownership for
wind turbine operators can be achieved by improved control of the wind turbines.
Wind turbine control can be improved in two ways, by improving the
model on which the controller bases its design or by improving the actual control
algorithm. Both possibilities have been investigated in this thesis.
The level of modeling detail has been expanded as dynamic in
ow has been
incorporated into the control design model where state-of-the-art controllers
usually assume quasi-steady aerodynamics. Floating wind turbines have been
suggested as an alternative to ground-fixed wind turbines as they can be placed
at water depths usually thought outside the realm of wind turbine placement.
The special challenges posed by controlling a floating wind turbine have been
addressed in this thesis.
Model predictive control (MPC) has been the foundation on which the control
algorithms have been build. Three controllers are presented in the thesis.
The first is based on four different linear model predictive controllers where appropriate
switching conditions determine which controller is active. Constraint
handling of actuator states such as pitch angle, pitch rate and pitch acceleration
is the primary focus of this controller. The wind turbine is a highly nonlinear
plant and a gain scheduling or relinearizing model predictive controller forms
the next step to improve performance compared to a linear controller. Finally, a
nonlinear model predictive controller has been devised and tested under simplified conditions. At present, the nonlinear model predictive controller is however
not expected to be an realistic option for real world application as the computation
burden is to heavy to achieve real-time performance.
This thesis is comprised of a collection scientific papers dealing with the various
topics presented in this summary.
- Aeroelastic design methods
- Wind Energy