Abstract
Wind energy is attractive in the presence of climate concerns and has the potential to dramatically reduce the dependency on nonrenewable energy resources. With the increase in wind farms there is a need to improve the efficiency in power allocation and power generation among wind turbines. In this paper, a hierarchical algorithm including a cooperative level and an individual level is developed for power coordination and planning in a wind farm. In the cooperative level, a constrained quadratic programming problem is formulated and solved to allocate the power to wind turbines considering the aerodynamic effects of wake interaction and the power generation capabilities of wind turbines. In the individual level, a method based on the local pursuit strategy is studied to connect the cooperative level power allocation and the individual level power generation using a virtual leader-follower scheme. The stability of individual wind turbine power generation is analyzed. Simulations are used to show the advantages of the method.
Highlights
Wind energy is considered to be an important player in the renewable energy market, enabling a reduction in carbon pollution from conventional energy with a rapid growth at the rate of around 27% per year between 2005-2009 [1] [2]
Promising in its potential, wind farms arranged in arrays suffer in power output due to aerodynamic interaction between the wind turbines
It is worth mentioning that all the wind turbines in the simulated wind farm are assumed to be the same, non-homogenous dynamics models can be used in the proposed cooperative control algorithm
Summary
Wind energy is considered to be an important player in the renewable energy market, enabling a reduction in carbon pollution from conventional energy with a rapid growth at the rate of around 27% per year between 2005-2009 [1] [2]. Promising in its potential, wind farms arranged in arrays suffer in power output due to aerodynamic interaction between the wind turbines. In individual wind turbine controls, work has been done on using linear/nonlinear feedback control techniques to track the power to be produced. An example of this can be seen in [7] where the researchers proposed an adaptive control strategy using neural network to control rotor speed and blade pitch angle. Another popular direction is the study of wind availability and the stability analysis of the system while switching between different operation regimes [8]
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