# Neural Network-Based Cost-Effective Estimation of Useful Variables to Improve Wind Turbine Control
## 4 Control Using Improved Aerodynamic Torque Estimation  
As shown in Figure 3 (Section 2.5), constant speeds are maintained in modes 1 and 3, which are buffering zones; the \(C_{pmax}\) curve is tracked to extract as much energy as possible from the wind in mode 2; and the wind turbine stalls to maintain the rated power in high wind speeds in mode 4. Figure 15: Scenario 5: Wind speed estimation from Turbine 5. The grid frequency, and thus the rotor speed, vary in response to the aerodynamic torque such that the operating strategy curve (Figures 3 and 16) is tracked. Generally, aerodynamic torque is estimated (rather than measured) by the conventional aerodynamic torque estimator (Section 3) from the measured generator torque. In Section 3, the simulation results demonstrate that the aerodynamic torque estimation is improved using the NN-based estimator. Here, a new controller that uses aerodynamic torque estimated by the NN-based estimator (Section 4) is presented and compared to the original controller that uses aerodynamic torque estimated by the conventional aerodynamic torque estimator (Section 3.2). Figures 17-19 show the behaviour of Turbine 5 (selected randomly because the other turbines behave similarly) at mean wind speeds of 8 (mode 2; refer to Figure 3 for \"modes\"), 10 (mode 3), and 14 (mode 4) m/s, respectively, in the time-domain when the controller uses the aerodynamic torque estimated by the NN-based estimator (in red) compared to the case where the controller uses the aerodynamic torque estimated by the conventional aerodynamic torque estimator (in black). As shown in these figures, it is not straightforward to compare the two because the controllers are designed to track the full operational strategy curve in the torque-speed plane; that is, the torque-speed plane shown in Figure 16 should be used to facilitate a clearer comparison between the two as follows. Figure 16 shows the behaviour of Turbine 5 (selected randomly because the remaining turbines behave similarly) in the torque-speed plane over the full operational envelope, that is, from 8 to 16 m/s with an increment of 2 m/s when the NN-based estimator (in red) provides the controller with the estimation of aerodynamic torque compared to when the conventional aerodynamic torque estimator (in blue) provides the controller with the estimation. As can be seen, the full operational strategy curve is tracked more closely with the NN-based estimator. At each mean wind speed (i.e., over the full envelope), the wind turbine is simulated for 700 s to generate the results shown in the figure. Here, each green curve represents a mean wind speed, as clearly shown in Figure 3. In each mode, the controller tracks the full operational strategy curve more closely when the controller uses the aerodynamic torque estimated using the NN-based estimator, as is clearly demonstrated in the torque-speed plane. Figure 16: Control operation results in torque-speed plane; refer to Figure 3 for the green wind speed curves. Figure 19: Output at a mean wind speed of 14 m/s (mode 4). Figure 17: Output at a mean wind speed of 8 m/s (mode 2). Figure 18: Output at a mean wind speed of 10 m/s (mode 3). Figure 20 shows the corresponding power efficiency of the controller for the below rated wind speed when the NN-based estimator (in red) is used, compared to when the conventional aerodynamic torque estimator (in blue) is used. These results demonstrate that efficiency is improved using the NN estimator. Here, the interpretation supports the results shown in Figure 16. Note that presenting the power efficiency in modes 3 and 4 is meaningless because the control role in these modes is to limit power rather than extracting as much power as possible. The results for Scenarios 1 to 5 are summarised in Table 1. Estimations acquired by the NN-based estimator tracking actual measurements are quantified and compared with estimations acquired by the conventional wind speed and aerodynamic torque estimators tracking actual measurements, for each scenario.