# Neural Network-Based Cost-Effective Estimation of Useful Variables to Improve Wind Turbine Control
## 5 Conclusions  
In this paper, a simple method for estimating potentially useful (but typically unmeasured) variables in wind turbines and farms is proposed. Only a single turbine in a cluster of several wind turbines (five in this paper) is equipped with a sensor, and the remaining turbines are equipped with an NN based estimator, which would significantly reduce the associated costs. The turbine equipped with the sensor is used to train the NN-based estimator, which essentially replaces potentially expensive sensors. The proposed method can be used to estimate various variables; however, this paper focuses on wind speed and aerodynamic torque. The results of simulations demonstrate that estimations acquired by the NN-based estimator closely follow actual measurements and are closer to actual measurements than estimates obtained by the conventional wind speed and aerodynamic torque estimators.  
Wind turbine controllers often utilise aerodynamic torque estimation, and this estimation is frequently performed using a conventional aerodynamic torque estimator, for example, the one described in Section 3.1. The new controller tested in this paper replaces the conventional aerodynamic torque estimator with the proposed NN-based estimator, and the resulting control output is compared to that of the original controller using the conventional aerodynamic torque estimator. The results demonstrate that the controller yields improved results when it uses the estimated aerodynamic torque obtained by the proposed NN-based estimator.  
Further research in this field will include improving the control of wind turbines by incorporating the wind speed estimated by the proposed method and utilising the NN-based estimation method to estimate other variables that are hard or expensive to measure, to further improve control and condition monitoring of wind turbines. Furthermore, in this paper it is assumed that the wind turbines are aligned side by side, implying that the wake effects are not dominant. Future work will consider various different layouts, which could amplify the wake effects, and investigate how the proposed method is affected.  
Conceptualization, S.-h.H. and Y.-s.R.; methodology, S.-h.H.; software, Y.-s.R.; validation, S.-h.H. and Y.-s.R.; formal analysis, S.-h.H.; investigation, S.-h.H. and Y.-s.R.; resources, S.-h.H. and Y.-s.R.; data curation, Y.-s.R.; writing--original draft preparation, S.-h.H. and Y.-s.R.; writing--review and editing, S.-h.H.; visualization, S.-h.H. and Y.-s.R.; supervision, S.-h.H.; project administration, S.-h.H.; funding acquisition, S.-h.H. Both authors have read and agreed to the published version of the manuscript.  
Figure 20: Power efficiency in below rated wind speed.  
Funding: This work was supported in part by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korean government (MOTIE) (20203010020010, Development and Demonstration of Integrated O&M Service Solution for Digital-Based Offshore Wind Power Plant) and the Korea Electric Power Corporation (R21X001-17).  
Data Availability Statement: Not applicable.  
The authors declare no conflict of interest.