In this paper, a new linear quadratic regulator (LQR) and proportional integral (PI) hybrid control algorithm for a permanent-magnet synchronous-generator (PMSG) horizontal-axis wind turbine was developed and simulated. The new algorithm incorporates LQR control into existing PI control structures as a feed-forward term to improve the performance of a conventional PI control. A numerical model based on MATLAB/Simulink and a commercial aero-elastic code were constructed for the target wind turbine, and the new control technique was applied to the numerical model to verify the effect through simulation. For the simulation, the performance data were compared after applying the PI, LQR, and LQR-PI control algorithms to the same wind speed conditions with and without noise in the generator speed. Also, the simulations were performed in both the transition region and the rated power region. The LQR-PI algorithm was found to reduce the standard deviation of the generator speed by more than 20% in all cases regardless of the noise compared with the PI algorithm. As a result, the proposed LQR-PI control increased the stability of the wind turbine in comparison with the conventional PI control.

A new wind farm control algorithm that adjusts the power output of the most upstream wind turbine in a wind farm for power increase and load reduction was developed in this study. The algorithm finds power commands to individual wind turbines to maximize the total power output from the wind farm when the power command from the transmission system operator is larger than the total available power from the wind farm. To validate this wind farm control algorithm, a relatively high fidelity wind farm simulation tool developed in the previous study was modified to include a wind farm controller which consists of a wind speed estimator, a power command calculator and a simplified wind farm model. In addition, the wind turbine controller in the simulation tool was modified to include a demanded power tracking algorithm. For a virtual wind farm with three 5 MW wind turbines aligned with the wind, simulations were performed with various ambient turbulent intensities, turbine spacing, and control frequencies. It was found from the dynamic simulation using turbulent winds that the proposed wind farm control algorithm can increase the power output and decrease the tower load of the most upstream wind turbine compared with the results with the conventional wind farm control.

This paper presents a modified version of the Ainslie eddy viscosity wake model and its accuracy by comparing it with selected exiting wake models and wind tunnel test results. The wind tunnel test was performed using a 1.9 m rotor diameter wind turbine model operating at a tip speed ratio similar to that of modern megawatt wind turbines. The control algorithms for blade pitch and generator torque used for below and above rated wind speed regions similar to those for multi-MW wind turbines were applied to the scaled wind turbine model. In order to characterize the influence of the wind turbine operating conditions on the wake, the wind turbine model was tested in both below and above rated wind speed regions at which the thrust coefficients of the rotor varied. The correction of the Ainslie eddy viscosity wake model was made by modifying the empirical equation of the original model using the wind tunnel test results with the Nelder-Mead simplex method for function minimization. The wake prediction accuracy of the modified wake model in terms of wind speed deficit was found to be improved by up to 6% compared to that of the original model. Comparisons with other existing wake models are also made in detail.

This study was conducted to analyze the impact of surrounding environmental changes on the feedback gain and performance of a closed-loop wind farm controller that reduces the error between total power output of wind farm and power command of transmission system operator. To analyze the impact of environment changes on wind farm controller feedback gain, the feedback gain was manually changed from 0 to 0.9 with a 0.1 interval. In this study, wind speed and wind direction changes were considered as environment changes; it was found by simulation code that the wind farm controller gain is in inverse proportion to wake recovery rate. In other words, the feedback gain should be higher if the distance between upstream and downstream wind turbine is not sufficient to wake recovery. Furthermore, the feedback gain should be lower when the upstream wind turbine generates a relatively weak wake by operating above the rated wind speed. The wind farm simulation was performed using reference 5 MW wind turbines from the National Renewable Energy Laboratory (NREL), which are numerically modeled for each element so that wind farm power output and tower load can be calculated according to the variation of the power command by using a modified wake model with improved accuracy. All the simulations performed in this study were carried out to review the power output accuracy of wind farms, but only if the transmission system operator’s power command was lower than the available power of wind farm. In this study, the gain of the wind farm controller was applied differently depending on the wind speed and direction to consider benefits in terms of power and tower load, especially if the wake effect of the upstream wind turbine was rapidly transferred to the downstream wind turbine. Ultimately, a simple, but more effective, power distribution method was proposed for distributing power commands to wind turbines that constitute wind farms and the study indicated the need for controller gain adjustment based on surrounding environmental changes.