## 5 Conclusions  
This work has been devoted to the numerical and data-driven study of a wind turbine control upgrade. The selected test case is a rotor rpm management optimization on a multi-megawatt wind turbine, from a wind farm sited in Italy in a very complex terrain, featuring 17 aerogenerators. There were at least two major motivations for this study. The former motivation was that the efficiency of wind turbine power upgrades can depend remarkably on site-specific conditions and it is therefore important to formulate reliable data-based models for assessing them and to collect test cases discussions. The latter motivation is that validating numerical frameworks for reliably simulating wind turbine control upgrades in a real environment would be a major step forward for the industry and for the scientific community as well. On these grounds, in this work, the test case was studied with a twofold approach. The objective was estimating the production improvement using, respectively, aeroelastic simulations and operational data before and after the upgrade. As regards the use of operation data, the adopted strategy was the formulation of a robust model for the power of the wind turbine of interest. The model has been trained with pre-upgrade data and validated against a pre-upgrade and a post-upgrade data set: If the model is precise enough to resolve the upgrade of interest, a difference in the behavior of the residuals between measurements and model estimates should be detected. In this work, the selected model for the power of the wind turbine of interest was a multivariate linear; a point of particular interest was the selection of the most appropriate input variables for the model: This was achieved using a stepwise regression algorithm and the selected input variables are powers and rotor rpms of nearby wind turbines. In the selected test case, the result was three reference wind turbines being employed. The reliability of the numerical simulations was based on mimicking the measured (before and after the upgrade) generator rpm-generator rpm curves. The FAST software represents this relation through approximations (quadratic, linear, and constant) and the functions that fit best the operational data were selected for the set up of the control system in the numerical environment. Once the pre- and post-upgrade models for the wind turbine of interest were tuned, simulations ere run spanning the interval of wind intensities from cut-in to rated power: Average wind intensities spaced 0.5 m/s were selected. The flow conditions on sitewere taken into account, by selecting a class A turbulence in the simulation of wind time series through the TurbSim package. The above discussion clarifies that the data-driven and numerical methods are based on very different philosophies. The former is more or less a black box, based on operational data and on the assumption of a linear relation between several input variables from reference wind turbines and the power of the wind turbine of interest. The latter moves from the principles of blade element momentum theory. It is interesting that these two different methods provide results that are of the same order of magnitude: The improvement is estimated as +1.0% of the production below rated power. These results therefore shed light on the possibility of successfully adopting numerical simulations for estimating the effects of wind turbine control upgrades. Furthermore, it should be noticed that a reliable numerical environment can be used in a predictive way. Specifically, for this kind of problems, simulations can be employed to estimate the modification of the mechanical loads induced by the control upgrade.