In this paper, we extend our closed-loop optimal control framework for wind farms to minimize wake-induced power losses. We develop an adjoint-based model predictive controller which employs a medium-fidelity 2D dynamic wind farm model. The wind turbine axial induction factors are considered here as the control inputs to influence the overall performance by taking wake interactions of the wind turbines into account. A constrained optimization problem is formulated to maximize the total power production of a given wind farm. An adjoint approach as an efficient tool is utilized to compute the gradient for such a large-scale system. The computed gradient is then modified to deal with the defined final set and practical constraints on the wind turbine control inputs. The performance of the wind farm controller is examined for a more realistic test case, a layout of a 2 x 3 wind farm with dynamical changes in wind direction. The effectiveness of the proposed approach is studied through simulations.@en

In this paper, a model predictive control (MPC) is proposed for wind farms to minimize wake-induced power losses. A constrained optimization problem is formulated to maximize the total power production of a wind farm. The developed controller employs a two-dimensional dynamic wind farm model to predict wake interactions in advance. An adjoint approach as an efficient tool is utilized to compute the gradient of the performance index for such a large-scale system. The wind turbine axial induction factors are considered as the control inputs to influence the overall performance by taking the wake interactions into account. A layout of a 2 × 3 wind farm is considered in this study. The parameterization of the controller is discussed in detail for a practical optimal energy extraction. The performance of the adjoint-based model predictive control (AMPC) is investigated with time-varying changes in wind direction. The simulation results show the effectiveness of the proposed approach. The computational complexity of the developed AMPC is also outlined with respect to the real time control implementation.@en

In this paper, a model predictive control (MPC) is proposed for wind farms to minimize wake-induced power losses. A constrained optimization problem is formulated to maximize the total power production of a wind farm. The developed controller employs a two-dimensional dynamic wind farm model to predict wake interactions in advance. An adjoint approach as an efficient tool is utilized to compute the gradient of the performance index for such a large-scale system. The wind turbine axial induction factors are considered as the control inputs to influence the overall performance by taking the wake interactions into account. A layout of a 2 × 3 wind farm is considered in this study. The parameterization of the controller is discussed in detail for a practical optimal energy extraction. The performance of the adjoint-based model predictive control (AMPC) is investigated with time-varying changes in wind direction. The simulation results show the effectiveness of the proposed approach. The computational complexity of the developed AMPC is also outlined with respect to the real time control implementation.@en

This paper proposes an adjoint-based model predictive control for optimal energy extraction of wind farms. It employs the axial induction factor of wind turbines to influence their aerodynamic interactions through the wake. The performance index is defined here as the total power production of the wind farm over a finite prediction horizon. A medium-fidelity wind farm model is utilized to predict the inflow propagation in advance. The adjoint method is employed to solve the formulated optimization problem in a cost effective way and the first part of the optimal solution is implemented over the control horizon. This procedure is repeated at the next controller sample time providing the feedback into the optimization. The effectiveness and some key features of the proposed approach are studied for a two turbine test case through simulations.@en

As the diameters of wind turbine rotors increase, the loads across the rotors are becoming more uneven due to inhomogeneous wind fields. Therefore, more advanced passive or active load reduction techniques are introduced to mitigate these uneven loads. Furthermore, measuring the disturbance can help to improve the control performance. This paper first examines how robust stability and performance are affected by uncertain sensor measurements when an integrator-based feedback is extended with an inversion-based feedforward individual pitch controller with similar bandwidth. A fixed-structured H∞feedback-feedforward controller is proposed. The proposed feedback-feedforward controller ensures robust stability and performance and achieves better load reduction than a classical integrator-based feedback controller combined with inversion-based feedforward controller.@en

As the diameters of wind turbine rotors increase, the loads across the rotors are becoming more uneven due to inhomogeneous wind fields. Therefore, more advanced passive or active load reduction techniques are introduced to mitigate these uneven loads. Furthermore, measuring the disturbance can help to improve the control performance. This paper first examines how robust stability and performance are affected by uncertain sensor measurements when an integrator-based feedback is extended with an inversion-based feedforward individual pitch controller with similar bandwidth. A fixed-structured H∞feedback-feedforward controller is proposed. The proposed feedback-feedforward controller ensures robust stability and performance and achieves better load reduction than a classical integrator-based feedback controller combined with inversion-based feedforward controller.@en

In this paper, an adjoint-based model predictive control (AMPC) is proposed in order to provide active power control (APC) services of wind farms, even in the presence of problematic wake interactions. The control objective is defined to minimize wind farm power reference tracking error. The non-unique optimal distribution of wind turbine power references is a resulting by-product which can be very informative for other wind farm control methods. The developed predictive controller employs a medium-fidelity 2D dynamic wind farm model to predict wake interactions at hub-height of wind turbines in advance. An adjoint approach as a computationally efficient tool is utilized to compute the gradient for such a large-scale system. The axial induction factor of each wind turbine is considered here as a control variable to influence the overall performance of a wind farm by taking the wake interactions of the wind turbines into account. The performance of the AMPC-based APC is examined for a layout of a 2×3 wind farm in a wake condition through simulation studies. The results show the effectiveness of the proposed approach and introduce some potential studies to improve and extend its performance.@en

In this paper, an adjoint-based model predictive control (AMPC) is proposed in order to provide active power control (APC) services of wind farms, even in the presence of problematic wake interactions. The control objective is defined to minimize wind farm power reference tracking error. The non-unique optimal distribution of wind turbine power references is a resulting by-product which can be very informative for other wind farm control methods. The developed predictive controller employs a medium-fidelity 2D dynamic wind farm model to predict wake interactions at hub-height of wind turbines in advance. An adjoint approach as a computationally efficient tool is utilized to compute the gradient for such a large-scale system. The axial induction factor of each wind turbine is considered here as a control variable to influence the overall performance of a wind farm by taking the wake interactions of the wind turbines into account. The performance of the AMPC-based APC is examined for a layout of a 2×3 wind farm in a wake condition through simulation studies. The results show the effectiveness of the proposed approach and introduce some potential studies to improve and extend its performance.@en

In this paper, an adjoint-based model predictive control (AMPC) is proposed in order to provide active power control (APC) services of wind farms, even in the presence of problematic wake interactions. The control objective is defined to minimize wind farm power reference tracking error. The non-unique optimal distribution of wind turbine power references is a resulting by-product which can be very informative for other wind farm control methods. The developed predictive controller employs a medium-fidelity 2D dynamic wind farm model to predict wake interactions at hub-height of wind turbines in advance. An adjoint approach as a computationally efficient tool is utilized to compute the gradient for such a large-scale system. The axial induction factor of each wind turbine is considered here as a control variable to influence the overall performance of a wind farm by taking the wake interactions of the wind turbines into account. The performance of the AMPC-based APC is examined for a layout of a 2×3 wind farm in a wake condition through simulation studies. The results show the effectiveness of the proposed approach and introduce some potential studies to improve and extend its performance.@en

In this paper, an adjoint-based model predictive control (AMPC) is proposed in order to provide active power control (APC) services of wind farms, even in the presence of problematic wake interactions. The control objective is defined to minimize wind farm power reference tracking error. The non-unique optimal distribution of wind turbine power references is a resulting by-product which can be very informative for other wind farm control methods. The developed predictive controller employs a medium-fidelity 2D dynamic wind farm model to predict wake interactions at hub-height of wind turbines in advance. An adjoint approach as a computationally efficient tool is utilized to compute the gradient for such a large-scale system. The axial induction factor of each wind turbine is considered here as a control variable to influence the overall performance of a wind farm by taking the wake interactions of the wind turbines into account. The performance of the AMPC-based APC is examined for a layout of a 2×3 wind farm in a wake condition through simulation studies. The results show the effectiveness of the proposed approach and introduce some potential studies to improve and extend its performance.@en

In this paper, an adjoint-based model predictive control (AMPC) is proposed in order to provide active power control (APC) services of wind farms, even in the presence of problematic wake interactions. The control objective is defined to minimize wind farm power reference tracking error. The non-unique optimal distribution of wind turbine power references is a resulting by-product which can be very informative for other wind farm control methods. The developed predictive controller employs a medium-fidelity 2D dynamic wind farm model to predict wake interactions at hub-height of wind turbines in advance. An adjoint approach as a computationally efficient tool is utilized to compute the gradient for such a large-scale system. The axial induction factor of each wind turbine is considered here as a control variable to influence the overall performance of a wind farm by taking the wake interactions of the wind turbines into account. The performance of the AMPC-based APC is examined for a layout of a 2×3 wind farm in a wake condition through simulation studies. The results show the effectiveness of the proposed approach and introduce some potential studies to improve and extend its performance.@en

In this paper, an adjoint-based model predictive control (AMPC) is proposed in order to provide active power control (APC) services of wind farms, even in the presence of problematic wake interactions. The control objective is defined to minimize wind farm power reference tracking error. The non-unique optimal distribution of wind turbine power references is a resulting by-product which can be very informative for other wind farm control methods. The developed predictive controller employs a medium-fidelity 2D dynamic wind farm model to predict wake interactions at hub-height of wind turbines in advance. An adjoint approach as a computationally efficient tool is utilized to compute the gradient for such a large-scale system. The axial induction factor of each wind turbine is considered here as a control variable to influence the overall performance of a wind farm by taking the wake interactions of the wind turbines into account. The performance of the AMPC-based APC is examined for a layout of a 2×3 wind farm in a wake condition through simulation studies. The results show the effectiveness of the proposed approach and introduce some potential studies to improve and extend its performance.@en