## 5 Conclusions  
In this study, wind speed estimation algorithms employing two methods were designed for the NREL 5 MW turbine. To validate the designed wind speed estimation algorithms, comparative verifications were conducted using the feed-forward control, available power estimation, and LQRF control. The first method is based on the drive train model, using measured rotor speed, pitch angle, and generator torque as inputs. The second method utilizes a continuous-discrete extended Kalman filter designed based on the Kalman filter. To validate the algorithms, dynamic simulations based on the Bladed program were performed, and wind speeds estimated using the two different methods were compared. The comparison results indicated that the wind speed estimation performances were similar; however, considering the design phase of the wind speed estimator, the Kalman filter-based wind speed estimation involves a more complex process, due to the use of relatively more variables. Firstly, according to simulation of the feed-forward control algorithm results, the algorithm based on the drive train model method reduced the standard deviations of rotor speed and output by 6.04% and 8.70%, respectively, while the Kalman filter-based feed-forward control algorithm reduced them by 2.05% and 1.93%, respectively. The reductions in standard deviation for rotor speed and output performance were relatively superior when the drive train model-based method was applied. Secondly, additional simulations were conducted using the available power estimation algorithm. The results were evaluated using the means and standard deviations of generator output and estimated available power. The mean errors were \(-0.58\%\) and 7.29%, and the standard deviation errors were 4.07% and 8.37%, respectively. Furthermore, according  
Figure 10: Dynamic simulation results with the application of the LQRF control algorithm. to the comparison of the RMSE, MSE, and MAE values, the drive train model-based method showed RMSE, MSE, and MAE values of 0.2061 MW, 0.0425 MW, and 0.1672 MW, respectively, while the Kalman filter method showed RMSE, MSE, and MAE values of 0.2332 MW, 0.0544 MW, and 0.1818 MW, respectively. The simulation results confirmed that the drive train model-based method exhibited relatively superior performance in estimating available power. Lastly, according to the simulation of the LQRF control algorithm results, the LQRF control algorithm using the Kalman filter-based method showed higher standard deviations of rotor speed and output, by 22.83% and 20.75%, respectively. Therefore, the performance of the LQRF control algorithm using the drive train model-based method was relatively superior. This study's results, although based on simulations, confirmed that applying different wind speed estimation methods led to varying performance outcomes for the target algorithms. It was found that the drive train model-based method had a more favorable impact on wind turbine control compared to the other method. Consequently, it was concluded that wind speed prediction accuracy must be considered to optimize wind turbine operational performance. For future research, experimental validation is planned using the wind speed estimation algorithms verified in this study, applied to a 100 kW medium-sized wind turbine, currently under development for reliability- and accuracy-related improvements on the research findings. Additionally, expanding research on performance validation by applying the estimated wind speeds to other control algorithms will be pursued. Conceptualization, D.K. ; methodology, D.K. and T.J.; software, D.K. ; supervision, I.P. ; validation, D.K. and T.J.; investigation, D.K. and W.R.; data curation, D.K. and T.J.; writing--original draft, D.K. ; writing--review and editing, I.P. All authors have read and agreed to the published version of the manuscript.