## 5 Conclusions  
The present study has been devoted to use of SCADA data for the diagnosis of electrical damage at wind turbine generators. As discussed in Section 1, this objective is particularly challenging because the sampling time of SCADA data is not the most appropriate for interpreting the dynamics of electrical phenomena of machines subjected to non-stationary conditions, as wind turbines are. Nevertheless, the widespread use of SCADA data and the potential applications in wind energy practice motivate a continuously growing scientific interest.  
The main result of this study is that appropriate SCADA data analysis methods are helpful in diagnosing electrical damage to wind turbine generators. This has been accomplished through the analysis of a real-world test case, which is the breakdown and the consequent replacement of a generator at a Vestas V52 wind turbine sited in southern Italy.  
The proposed methodology is based on the construction of normal behavior models for the power and for the voltages and currents of the wind turbine. The relevant features were selected based on their Pearson correlation coefficient with the target and the PCA was employed to reduce the dimension of the problem and to deal with the collinearity of the regressors. The non-linear relation between the features and the target was taken into account using support vector regression with a Gaussian kernel.  
Through the analysis of how the mismatch between model estimates and measurements evolved approaching the time of the fault, it was possible to individuate the damage with an advance in the order of two weeks. Considering the nature of the damage and the type of employed data, this result is very promising as regards the use of SCADA data for monitoring the health status of wind turbine generators.  
A valuable further direction of the present work would be the use of time-resolved SCADA data with a sampling time of the order of the second [37]. Despite the fact that in wind energy practice they are more complex to manage from the OPC-DA servers of wind turbines, these kinds of data could likely provide a deeper insight into the dynamics of wind turbines and this could further improve the diagnostic capability of data-driven methods.  
In general, a realistic application for the type of analysis presented in this work is to constitute a first advice for the assessment of generator conditions, based on which it could  
Figure 7: The time history of the difference between Tar—Ref normalized mean absolute error (NMAE) with different statistical thresholds.  
be possible to plan devoted inspections using methodologies similar to those in [8]. From this point of view, the present study represents a contribution to the methodologies for condition-based maintenance of wind turbine generators.  
Conceptualization, D.A., F.C., F.N.; data curation, D.A., F.C., F.N.; formal analysis, D.A., F.C., F.N.; investigation, D.A., F.C., F.N.; methodology, D.A., F.C., F.N.; software, D.A., F.C., F.N.; supervision, F.C., validation, D.A., F.C., F.N.; writing--original draft, D.A.; writing--review editing, F.C., F.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors thank the company Lucky Wind Spa for the technical support and for providing the data sets employed for the study.  
The authors declare no conflict of interest.