# Neural Network-Based Cost-Effective Estimation of Useful Variables to Improve Wind Turbine Control
## 1 Introduction  
Operation and maintenance (O&M) costs account for a significant proportion of the total annual costs of a wind turbine. Typically, O&M costs account for more than 20% of the total levelised cost per kWh. Therefore, in recent years, reducing O&M costs, which include control and condition monitoring, has become even more important in wind turbine and farm operational strategies because it has a long-lasting impact on the profitability and efficiency of wind site operations. Providing cost-effective access to various different variables in wind turbines and farms could help to reduce O&M costs by improving control and condition monitoring capabilities. Allowing access to variables that are normally not measured could, in fact, increase the costs because additional potentially expensive sensors might need to be introduced. However, the proposed approach is cost effective as it only requires a single sensor between multiple turbines. More precisely, it is proposed that one sensor be shared between multiple turbines (five in this paper), that is, one turbine is equipped with a potentially expensive sensor and each of the remaining turbines (four in this paper) is equipped with an estimator. Each estimator is based on a neural network (NN) [1, 2]. Note that it is often not feasible to exploit more conventional estimators, such as observers [3, 4] including extended Kalman filters [5, 6], since the behaviour of a wind turbine is highly nonlinear, and designing such observers would require a highly sophisticated mathematical model. Therefore, an NN is adopted to design nonlinear estimators that could replace sensors. The proposed method could be adopted for estimating various different useful variables; however, this study focuses on two variables: wind speed and aerodynamic torque. Wind speed and aerodynamic torque estimated by the NN-based estimators are comparedwith those estimated by conventional wind speed and aerodynamic torque estimators, respectively. Many existing wind turbine controllers require the estimation of aerodynamic torque, which is normally conducted by conventional aerodynamic torque estimators, such as the one introduced in [7]. In this paper, a new controller is introduced that uses aerodynamic torque estimated by the NN-based estimator and is compared with the original controller, which uses aerodynamic torque estimated by the conventional aerodynamic torque estimator, demonstrating improved results. Wind speed estimated by the proposed NN-based estimator could also help to improve wind turbine control; however, that is not discussed in this paper. Most estimation-related work in the wind turbine control community deals with wind speed estimation, such as the work presented in [8; 9; 10]. A detailed survey on this topic is presented in [11]. The NN-based method proposed in this paper is novel, as no other existing work realises wind speed estimation at a wind farm level taking into account the associated costs. Modern control topics currently of interest in the wind turbine control community include improving the control performance by incorporating wind speed (normally measured using a LiDAR) into the controller design, such as in [12; 13; 14; 15]. However, the estimation method proposed here allows the use of not only wind speed but also other useful variables that are normally not measured as part of the controller design. As an example, the second part of this paper demonstrates potential improvement of the control performance, which can be achieved by improving the accuracy of aerodynamic torque estimation. In summary, the primary contributions of this paper are the development of NN-based estimators (focusing on the estimation of wind speed and aerodynamic torque here) and the improvement of the wind turbine controller realised by incorporating the improved estimation of aerodynamic torque in the original controller design.