## 1 Introduction
[PERSON] designed a wind speed estimation algorithm and a feed-forward control algorithm for a multi-MW wind turbine and performed a simulation based on a numerical model. The analysis results indicated that the feed-forward control algorithm effectively reduced the standard deviations of both rotor speed and generator output by approximately 50% [8]. [PERSON] designed an LQR controller based on fuzzy logic for the NREL 5 MW model and conducted simulations using the Bladed program. Additionally, the same algorithm was applied to a scaled-down wind turbine model, and experimental verification was conducted in a wind tunnel. The experimental results showed that the standard deviations of rotor speed and generator output were reduced by approximately 36.39% and 38.94%, respectively [14]. In recent research on available power estimation for the application of wind farm control algorithms, a comparative analysis of various wind speed estimation algorithms based on simplified wind turbine models was conducted [18]. In this study, a wind speed estimation algorithm based on a drive train model was compared with a simpler model-based wind speed estimation algorithm that excludes the time derivative term of the drive train model. The comparison results indicated that the simpler model-based wind speed estimation algorithm is more advantageous for available power estimation. Some of the literature has proposed Kalman filter-based methods that do not use the drive train model of wind turbines. [PERSON] et al. proposed a wind speed estimation algorithm using a non-standard extended Kalman filter to enhance wind turbine performance, confirming improved maximum power control performance through optimal tip speed ratio (TSR) tracking. According to simulation results, applying the proposed wind speed estimation algorithm to optimal TSR tracking control could increase annual energy production by approximately 0.8% [24]. [PERSON] et al. proposed a wind speed estimation algorithm based on the extended Kalman filter and applied it to a floating wind turbine control algorithm. Simulation results under average 11 m/s conditions indicated a root mean square error (RMSE) of approximately 0.48 m/s for estimated wind speed based on average rotor speed. The estimated wind speed was utilized for optimal TSR tracking control, torque control, and minimum pitch angle calculation for peak shaving, the application of which reduced rotor thrust in the Region II-1/2 area by approximately 20% [23]. As previously described, various studies have been presented in the literature on the application and validation of wind turbine controllers using either drive train model or Kalman filter-based estimated wind speeds. However, there is a lack of research comparing and validating the differences in wind speed prediction accuracy and controller performance between these two methods. There is a need for research that results in selecting the most suitable wind speed estimation method for control algorithms in order to enhance the operational performance of wind turbines. Therefore, this study aims to compare the prediction accuracy of the drive train model-based wind speed estimation algorithm with that of the Kalman filter-based wind speed estimation algorithm, proposed in the literature for application in wind turbine control, and to compare their performance when applied to the same controller. Performance comparisons were conducted focusing on feed-forward control algorithms for classic control, available power estimation algorithms for pitch control, and the linear quadratic regulator based on the fuzzy logic (LQRF) control algorithm. The aforementioned algorithms, previously proposed in other studies, were applied and validated on the selected NREL 5 MW model, specifically chosen for this research. The originality and contribution of this study, compared to existing research, can be summarized as follows.