TY - GEN
T1 - Wind Farms: Modeling and Control
AU - Soleimanzadeh, Maryam
PY - 2012
Y1 - 2012
N2 - The primary purpose of this work is to develop control algorithms for wind farms to optimize the power production and augment the lifetime of wind turbines in wind farms.In this regard, a dynamical model for wind farms was required to be the basis of the controller design. In the first stage, a dynamical model has been developed for the wind flow in wind farms. The model is based on the spatial discretization of the linearized Navier-Stokes equation combined with the vortex cylinder theory. The spatial discretization of the model is performed using the Finite Difference Method (FDM), which provides the state space form of the dynamic wind farm model. The model provides an approximation of the behavior of the flow in wind farms, and obtains the wind speed in the vicinity of each wind turbine.The control algorithms in this work are mostly on the basis of the developed wind farm model. The wind farm control algorithms provide reference signals for the controller of each wind turbine as commands. The reference signals are provided such that the demanded power from the whole farm is satisfied and also the structural loads on wind turbines are minimized. Three different control strategies have been addressed in this work, two centralized controller with two different strategies and one distributed controller.In the fist strategy for centralized control, the reference signal is determined forindividual wind turbines such that the load acting on wind turbines in low frequencies is minimized. The controller is practically feasible. Yet, the results on load reduction in this approach are not very significant.In the second strategy, the wind farm control problem has been divided into below rated and above rated wind speed conditions. In the above rated wind speed pitch angle and power reference signals are provided by the wind farm controller, whereas in below rated, the rotor speed reference signals are determined for maximum power production and load reduction. The structural loads in this strategy are dynamic loads, the tower foreaft and side-to-side motion of the single wind turbines which change persistently with the wind speed variations. Therefore, the controller has to perform all the computations in a relatively fast sampling rate which makes it difficult to implement practically. However, the controller has a very satisfactory results in terms of covering the required total power and load reduction.The third approach is distributed control of wind farm using the wind farm intrinsic distributed structure; where, the wind turbines are counted as subsystems of the distributed system. The coupling between the subsystems is the wind flow and the power demand of the wind farm. Distributed controller design commences with formulating the problem, where a structured matrix approach has been put in to practice. Afterwards, an H2 control problem is implemented to obtain the controller dynamics for a wind farm such that the structural loads on wind turbines are minimized.
AB - The primary purpose of this work is to develop control algorithms for wind farms to optimize the power production and augment the lifetime of wind turbines in wind farms.In this regard, a dynamical model for wind farms was required to be the basis of the controller design. In the first stage, a dynamical model has been developed for the wind flow in wind farms. The model is based on the spatial discretization of the linearized Navier-Stokes equation combined with the vortex cylinder theory. The spatial discretization of the model is performed using the Finite Difference Method (FDM), which provides the state space form of the dynamic wind farm model. The model provides an approximation of the behavior of the flow in wind farms, and obtains the wind speed in the vicinity of each wind turbine.The control algorithms in this work are mostly on the basis of the developed wind farm model. The wind farm control algorithms provide reference signals for the controller of each wind turbine as commands. The reference signals are provided such that the demanded power from the whole farm is satisfied and also the structural loads on wind turbines are minimized. Three different control strategies have been addressed in this work, two centralized controller with two different strategies and one distributed controller.In the fist strategy for centralized control, the reference signal is determined forindividual wind turbines such that the load acting on wind turbines in low frequencies is minimized. The controller is practically feasible. Yet, the results on load reduction in this approach are not very significant.In the second strategy, the wind farm control problem has been divided into below rated and above rated wind speed conditions. In the above rated wind speed pitch angle and power reference signals are provided by the wind farm controller, whereas in below rated, the rotor speed reference signals are determined for maximum power production and load reduction. The structural loads in this strategy are dynamic loads, the tower foreaft and side-to-side motion of the single wind turbines which change persistently with the wind speed variations. Therefore, the controller has to perform all the computations in a relatively fast sampling rate which makes it difficult to implement practically. However, the controller has a very satisfactory results in terms of covering the required total power and load reduction.The third approach is distributed control of wind farm using the wind farm intrinsic distributed structure; where, the wind turbines are counted as subsystems of the distributed system. The coupling between the subsystems is the wind flow and the power demand of the wind farm. Distributed controller design commences with formulating the problem, where a structured matrix approach has been put in to practice. Afterwards, an H2 control problem is implemented to obtain the controller dynamics for a wind farm such that the structural loads on wind turbines are minimized.
M3 - PhD thesis
SN - 978-87-92328-89-2
ER -