# Robust Control Examples Applied to a Wind Turbine Simulated Model
## 4 Simulations and Comparisons
### Final Remarks  
The application examples considered in this paper have highlighted how the availability of an accurate simulator of a wind turbine system has reached its maturity level, as shown in Section 2. Therefore, most of the insight has been given into the control techniques, resulting in advanced and refined solutions, which can include a variety of sub-functions, such as the optimal set-point generation, the set-point following and supervisory tasks, when considering the different operation requirements. In addition to these aspects, advanced control strategies relying on two approaches, such as data-driven and model-base strategies, have been considered for comparison purposes, as articulated in Section 3. It is clear that further 'levels' of control can be required in wind turbine applications, which were not considered in this study. There is a top level of supervisory control that verifies the available energy source and can limit the operation of the device accordingly, also in case of extreme conditions. These limitations may be implemented in order to preserve the system integrity, ensure safe operation or be required by legislation, as for the case of wind turbines and their cut-out wind speed limits. This situation is represented in Figure 3b, when the wind turbine operates in full load conditions (above the rated wind speed). On the other hand, it is also developed to work for generating electric energy when the wind speed is below its rated wind speed and an optimal reference has to be tracked, as highlighted by Equation (10). In this case, the maximum-energy transfer is required. However, wind turbine plants can also work outside their nominal operating conditions, and some supervisory module may be required to ensure the device integrity. This supervisory strategy is important, and it can represent the key point also for the safety of these energy conversion systems, as discussed by the authors, e.g., in [47], but not considered here. Control systems remain often hidden, since they are incorporated within the devices themselves. They are fundamental for energy conversion systems and can represent the key point technologies for achieving optimal performance, improving energy conversion and guaranteeing safety. Moreover, the solution of incorporating control technology is very appealing for several systems, since the addition of extra control features may be obtained by simply adding software functions [48]. In some cases, this solution avoids the requirement of expensive additional sensors and actuators [49]. However, this relatively straightforward implementation can require both sophisticated control methodologies and complicated mathematical formulations. For example, concerning wind turbines,  
Figure 11: Simulations in full load with the adaptive controllers. many high-performance model-based control schemes can require high-fidelity simulators of the controlled plants [50]. Nevertheless, there is usually a trade-off between the incorporation of advanced control techniques and the requirement of expensive technology, both used to improve the performance, reliability and safety of the monitored systems. In particular with reference to the proposed strategies, some further comments can be drawn in general, concerning the key aspects of these advanced control solutions, with respect to the baseline PID governors proposed in Section 2. The \(NSSE\%\) values obtained with the proposed control strategies are lower. Standard industrial controllers, such the classic PID regulators recalled in Section 2, are quite simple and have the benefit of quite straightforward tuning of their parameters. However, when exploited for controlling nonlinear dynamic processes, the control laws may lead to limited performances. Therefore, this point motivates the use of more advanced control solutions, as highlighted by the results summarized in this section. In particular, when the modeling of the dynamic process can be perfectly achieved, model-based control strategies generally represent the best option.