In this study, a linear quadratic regulator based on the fuzzy logic (LQRF) control algorithm for a variable-speed variable-pitch wind turbine was designed. In addition, to verify the optimum performance of the controller, simulations and wind tunnel tests were conducted. In the simulation, the performances of the proportional-integral (PI) and LQRF algorithms were compared in the transition region and the rated power region. In the wind tunnel test, the applicability of the LQRF algorithm was verified by comparing it with the conventional PI algorithms. The results showed that when compared with the PI control, the proposed LQRF control reduced the tower vibration by up to 12.50% depending on the operating region. Furthermore, the power deviation was reduced by 38.93%. These tests confirmed that the proposed LQRF control increases the power performance and structural stability of wind turbines compared with conventional PI controls.

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.

To compare and validate wind speed estimation algorithms applied to wind turbines, wind speed estimators were designed in this study, based on two methods presented in the literature, and their performance was validated using the NREL 5MW model. The first method for wind speed estimation involves a three-dimensional (3D) look-up table-based approach, constructed using drive train differential equations. The second method involves applying a continuous–discrete extended Kalman filter. To verify and compare the performance of the algorithms designed using these different methods, feed-forward control algorithms, available power estimation algorithms, and a linear quadratic regulator, based on fuzzy logic (LQRF) control algorithms, were selected and applied as verification means, using the estimated wind speed as the input. Based on the simulation results, the performance of the two methods was compared. The method using drive train differential equations demonstrated superior performance in terms of reductions in the standard deviations of rotor speed and electrical power, as well as in its prediction accuracy for the available power.

In this study, a reference bias control (RBC) algorithm for variable speed and variable pitch wind turbines was designed and validated. To improve the performance of conventional PI control algorithms, the RBC algorithm applies biased references to power and pitch angle to the pitch and the torque control loops, respectively. To validate the control performance of the improved RBC algorithm, hardware in the loop simulator (HILS) was conducted using a commercial programmable logic controller (PLC). The performance of a conventional PI control algorithm and the proposed RBC algorithm were compared for the target wind turbine model in terms of both the transition region and the rated power region. In the transition region, the proposed RBC algorithm improved the sudden dips in the generator torque and power, which often occur when using a control algorithm with a switching logic. As a result, the damage equivalent load (DEL) of the main shaft was reduced by 15%. In the rated power region, the rotor speed deviation was reduced by 22% and the power deviation was reduced by 21%. To experimentally validate the control performance and applicability of the RBC algorithm, wind tunnel testing using a wind turbine scaled model was additionally performed. Similarly to the HILS testing result, it was confirmed that the DEL of the main shaft and fluctuation of the rotor speed and power decreased with the proposed RBC algorithm.

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.

In this study, an $H_{\infty }$-fuzzy controller is proposed for application in wind turbines with uncertainties and nonlinearities. The performance of the proposed controller was validated via dynamic simulations using a commercial aero-elastic code and wind tunnel experiments employing a scaled wind turbine. The simulation and the experimental results were then compared with those of the conventional PI and LQR control algorithms presented in our previous study. In the simulation, the perturbation and the sensor noise were applied to reflect uncertainty and nonlinearity effects. In addition, in the wind tunnel experiment, a control system using a commercial Bachmann PLC was established with an accelerometer to estimate the fatigue load exerted by the rotor thrust. It was confirmed through experiments that the robustness and adaptation of the control system improved in the situation of pitch system failure. As a result of the experiment, the proposed $H_{\infty }$ controller was able to reduce the rotor speed fluctuation by 39.9%, the power fluctuation by 32.0%, and the fatigue load by 2.4% compared with the LQR fuzzy controller, which had better performance than the conventional PI controller. In addition, it was confirmed through experiments that the robustness and adaptation of the control system were well maintained. This was even true in the situation of one-blade pitch system failure.