In this study, the performance of an Archimedes spiral wind turbine is analyzed by simulation and validated by a field test. It is characterized as a horizontal-axis drag-type wind turbine. This type of wind turbine cannot be analyzed by the well-known Blade Element Momentum(BEM) theory or Double Stream Tube Method(DSTM) commonly used to analyze the performance of lift-type wind turbines. Therefore, the computational fluid dynamics (CFD) method was applied. From the simulation, the power coefficient, known as the mechanical efficiency of the rotor, the tip speed ratio was obtained. The maximum power coefficient, and the corresponding tip speed ratio were found to be 0.293 and 2.19, respectively. In addition, the electrical efficiency with respect to the rotational speed of the generator was obtained through generator–controller test. The obtained mechanical and electrical efficiencies were used to predict the power curve of the wind turbine. Finally, the predicted performance of the wind turbine, including the electrical losses, was validated by the field test. The maximum error between the prediction and the measured power was found to be less than 7.80%.

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, 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.

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.

Three different algorithms that can be used to estimate the available power of a wind turbine are investigated and validated in this study. The first method is the simplest and using the power curve with the measured nacelle wind speed. The other two are to estimate the equivalent wind speed first without using the measured Nacelle wind speed and to estimate the available power from the rotor power equation. The two methods are different in that the second method is to use the drive-train model to estimate the rotor torque but the third method is to use a simplified equation to avoid sharp peaks in the wind speed estimation. Simulations were performed to validate the constructed available power estimation algorithms with the measured data of a 2 MW target wind turbine. It was found from the validation that the third available power estimation algorithm works properly and is closer to the power actually generated from the wind turbine than the other methods considered. In addition, the third algorithm that showed the best performance was further validated with the DPPT (demanded power point tracking) operation with Matlab/Simulink environment. It was found from the simulation that the third algorithm works well in the DPPT operation to estimate the available power of the wind turbine.

In this study, a power compensation control algorithm was designed and validated for commercial 100 kW medium wind turbine models for power compensation due to additional generator loss. Generally, torque control considering generator efficiency is applied to a controller of a medium wind turbine; however, a control corresponding to a decrease in generator efficiency due to the surrounding environment is not possible. There is a possibility that an additional generator loss may occur due to the surrounding environment of the wind turbine already installed, and accordingly, a power compensation control algorithm is required because power is expected to decrease. The power compensation control algorithms may be divided into three methods according to a control strategy, and three power compensation control algorithms were explained and designed. The proposed power compensation control algorithms were validated using DNV’s Bladed program. The simulation conditions were selected at an average wind speed of about 18 m/s and normal turbulence model (NTM) Class A, and the additional generator loss was assumed to be 15%. The simulation comparison showed that the original power control algorithm had a deviation of 15.00% from the rated power due to a 15% generator loss, and the designed three power compensation control algorithms had a deviation of up to 0.05%.