## 1 Introduction  
The control algorithm of a wind turbine plays a crucial role in determining its operational performance. Control algorithms can be classified into the two following types: power control and load reduction control. Power control can be further divided into generator torque control and blade pitch control, depending on the type of control command signal. Generator torque control refers to the maximum power point tracking (MPPT)-based torque command adjustment to produce maximum power in the operating region with wind speeds lower than the rated wind speed, known as Region II. Blade pitch control, on the other hand, refers to the pitch angle command adjustment to maintain the rated rotor speed and power in the operating region with wind speeds higher than the rated wind speed, known as Region III [1, 2, 3]. Load reduction control is a control strategy aimed at reducing thrust and moments acting on the wind turbine, and algorithms based on it can be designed through the application of additional control logic for specific purposes [4]. Recently, control strategies that consider the dynamic behavior of the wind turbine drive train have been introduced, addressing issues related to fatigue damage in wind turbines [5]. Typically, wind turbine control algorithms include basic power control algorithms supplemented with classical single-input-single-output (SISO) control algorithms, such as proportional (P), proportional-integral (PI), and proportional-integral-derivative (PID) control techniques, for objectives including vibration or load reduction. Additionally,modern multi-input-multi-output (MIMO) control algorithms have been proposed to improve the performance of multi-loop SISO-based classical control logic [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. Classical control-based vibration and load reduction techniques include drive train dampers [4], feed-forward control algorithms [6; 7; 8], individual pitch control algorithms [9; 10], peak shaving [11], and tower dampers [12]. Modern control techniques based on MIMO algorithms include optimal control [14; 15] and robust control [16; 17]. Some classical control-based algorithms with SISO, as well as modern control algorithms, utilize wind speed obtained through wind speed estimation algorithms as essential parameters [13; 14; 15; 16]. Wind speed estimation algorithms for wind turbines refer to algorithms that estimate the equivalent wind speed acting on the current target wind turbine, using parameters measured from the wind turbine and a simplified wind turbine model [8; 18]. Wind speed estimation is generally performed via the wind turbine controller, and the estimated wind speed is used for various purposes depending on the algorithm; for instance, it can be used to calculate the available power at individual wind turbines for wind farm control, as parameters for feed-forward control algorithms to improve operational performance, and as one of the input parameters for modern control algorithms based on MIMO. The prediction accuracy of thusly estimated wind speed can influence control performance, necessitating research to investigate and improve prediction accuracy [19; 20; 21; 22]. There are two primary methods for estimating wind speed within wind turbine control algorithms using measured parameters to integrate and apply wind turbine control algorithms. The first method is a 3D look-up table-based estimation method, constructed using the aerodynamic torque calculation formula and drive train differential equations, which estimates wind speed using generator torque, blade pitch angle, and rotor speed as inputs [8; 18]. The second method employs an extended Kalman filter, designed based on a 3D wind field model and a nonlinear rotor model, as applied in the recently presented reference open-source controller (ROSCO) [23]. The drive train model-based estimation method has been applied as an input parameter in several control algorithm cases presented in the literature.