In this study, a pitch H-infinity control algorithm was developed for variable-speed–variable-pitch (VSVP) wind turbines to improve the rotor standard deviation of the wind turbines under normal and extreme wind conditions. The pitch H-infinity control algorithm only uses H-infinity control in the blade pitch control loop in the rated power region, and conventional torque gain scheduling algorithms are applied in the partial power region. The performance of this controller was verified using simulations of a 5 MW wind turbine using the commercial aeroelastic simulation code Bladed. The performance of the pitch H-infinity controller was compared with that of the conventional proportional-integral (PI) control algorithm under three different operating conditions: normal operating conditions without sensor noise, normal operating conditions with sensor noise, and extreme operating conditions without sensor noise based on the wind turbine design standard by IEC. Based on the simulation results with two different wind speed regions, namely, the transition region and the rated power region, it was found that the proposed pitch H-infinity controller showed better rotor speed standard deviation performance in the three operating conditions and achieved lower standard deviations of the rotor speed and the electrical power without affecting the mean electrical power.

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.

In this study, dynamic simulations of a wind turbine were performed to predict its dynamic performance, and the results were experimentally validated. The dynamic simulation received time-domain wind speed and direction data and predicted the power output by applying control algorithms. The target wind turbine for the simulation was a 2 MW wind turbine installed in an onshore wind farm. The wind speed and direction data for the simulation were obtained from WindSim, which is a commercial computational fluid dynamics (CFD) code for wind farm design, and measured wind speed and direction data with a mast were used for WindSim. For the simulation, the wind turbine controller was tuned to match the power curve of the target wind turbine. The dynamic simulation was performed for a period of one year, and the results were compared with the results from WindSim and the measurement. It was found from the comparison that the annual energy production (AEP) of a wind turbine can be accurately predicted using a dynamic wind turbine model with a controller that takes into account both power regulations and yaw actions with wind speed and direction data obtained from WindSim.

In this study, a demanded power point tracking (DPPT) control algorithm was designed for the application of multiple-input multiple-output (MIMO) modern control algorithms. The proposed DPPT control algorithm has been newly implemented as a multiple reference trajectory method for applying an MIMO control algorithm without mode switches. Dynamic simulations and wind tunnel experiments were performed using a scaled wind turbine to validate the proposed control algorithm. The wind speeds were 4.6 and 7.3 m/s, the average wind speeds corresponding to region 2 and region 3, respectively, with a turbulence intensity of 10%. Both sets of results demonstrated satisfactory performance for tracking the power commands transmitted from the wind farm controller. Furthermore, the proposed control algorithm was compared and validated with a DPPT control algorithm proposed in previous studies, and its improved control performance and validity were confirmed.