# Adaptive Pitch Controller of a Large-Scale Wind Turbine Using Multi-Objective Optimization
## 1 Introduction
There is a wide variety of studies using feedforward pitch control in wind turbines to reduce fatigue in different parts, such as the longitudinal bending moment of the tower base [19; 20; 21]. As for the ATDC case, feedforward is usually combined with a closed loop proportional-integral-derivative (PID) control to take advantage of both strategies. This can significantly improve the performance of the system in those cases in which the disturbance can be measured before affecting the controlled variable. Figure 1: Operation regions of a wind turbine [9]. Considering all the control elements discussed previously, computational intelligence methods seem to be a practicable alternative to address wind turbine control problems. These methods such as Artificial Neural Networks (ANN) and the Genetic Algorithm (GA) are well known to be computational tools to improve the performance of control techniques [22]. As recent applications in wind turbines, in [23] the use of a neuro-estimator based on neural networks is implemented, obtaining relevant improvements in the performance of the wind turbine pitch control. In another work [24], a neural controller for wind turbine pitch control based on a radial basis function (RBF) network is compared with a tuned PID obtaining better results. There are other works in technical optimization on wind turbines where the use of genetic algorithms helps the artificial neural network to reduce the computational cost and obtain better results in the optimization [25]. Wind turbine control problem can also be treated as a multi-objective problem considering a trade-off between optimizing power performance and lowering the structural loads on the wind turbine [26]. The Multi-Objective Genetic Algorithm (MOGA) method leads always to a correct and accurate identification of the whole Pareto front [27]. The results obtained by implementing the MOGA method do not depend on the analyzed objective and constraint functions. The drawback of the MOGA method is the large number of iterations required and, consequently, the large computational effort required to identify the Pareto front [27, 28]. Recent works use multi-objective optimization techniques to obtain a more efficient control of wind turbines with the help of emulated models. In [29], a coordination strategy between the blade pitch controller and generator torque and power controller is proposed through the Pareto optimization theory. In another work [30], a multi-objective optimization using a genetic algorithm is used to provide an optimal selection of the orthogonal TLCDs designs which reduce the fore-aft and side-to-side fatigue and extreme loads. Other scientific works use this methodology applied on real wind turbines, although the latter are scarcer. In [31], an intelligent optimization method has been proposed to optimize the potential performance though yaw control strategy of a real wind turbine manufactured by China Ming Yang Wind Power (CMYWP). In this case, the Pareto front had two conflicting objectives: the minimization of the power reduction factor and the minimization of the yaw actuator. The testing of optimization and control strategies applied directly to real wind turbines is unusual [32]. There is software that allows representing the aeroelastic and dynamic characteristics of a wind turbine without physically depending on the natural resources of the wind and a real wind turbine. Wind energy industry often uses aeroelastic simulators for horizontal-axis wind turbines and can analyze its turbines. FAST software is the primary physics-based engineering tool created by National Renewable Energy Laboratory (NREL) for simulating the coupled dynamic response of wind turbines. Germanischer Lloyd (GL) WindEnergie GmbH, the world's foremost certifying body for wind turbines and currently called Det Norske Veritas (DNV), issued a statement that it is acceptable for manufacturers to use the NREL codes for wind turbine certification [32].