Motivated by the reduction of overall wind power cost, considerable research effort has been focused on enhancing both efficiency and reliability of wind turbines. Maximizing wind energy capture while mitigating fatigue loads has been one of the main goals for control design. Recent developments in remote wind speed measurement systems (e.g., light detection and ranging (LIDAR)) have paved the way for implementing advanced control algorithms in the wind energy industry. In this paper, an LIDAR-assisted economic model predictive control (MPC) framework with a real-time adaptive approach is presented to achieve the aforementioned goal. First, the formulation of a convex optimal control problem is introduced, with linear dynamics and convex constraints that can be solved globally. Then, an adaptive approach is proposed to reject the effects of modelplant mismatches. The performance of the developed control algorithm is compared to that of a standard wind turbine controller, which is widely used as a benchmark for evaluating new control designs. Simulation results show that the developed controller can reduce the tower fatigue load with minimal impact on energy capture. For model-plant mismatches, the adaptive controller can drive the wind turbine to its optimal operating conditions while satisfying the optimal control objectives.

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Wind energy is a clean and renewable source for electricity generation. To reduce the costs associated with wind power generation, development of a control methodology that maximizes the wind energy capture and mitigates the turbine fatigue loading is desired. In this paper, a new adaptive gain modified optimal torque controller (AGMOTC) for wind turbine partial load operation is presented. A gain-scheduling technique with an internal proportional integral (PI) control is developed to accelerate the controller's convergence to a reference tip speed ratio (TSR). The reference TSR is then adjusted to its optimal value in real-time through an adaptive algorithm capable of rejecting model uncertainties and estimation errors of the control gain. A fatigue mitigation method is also designed to reduce the impact of exacerbated tower bending moments due to the resonance effect. The proposed AGMOTC is evaluated based on the National Renewable Energy Laboratory (NREL) 5 MW wind turbine model using the NREL fast simulator. Simulation results have shown that the AGMOTC has improved efficiency and robustness in wind energy capture and reduced tower fatigue loading as compared to the traditional control technique.