# Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines
## 3 Results
### Experiments for the Gearbox
\begin{table}
\begin{tabular}{l l} \hline \hline
**GearBox System Model** & **Description** \\ \hline wgdc\_avg\_TrGi\_PwrAt/wgdc\_avg\_TrGi\_PF & Gross power output of the turbine, in KW \\ wrtm\_avg\_TrmTmp\_GbxOil & Temperature of the gearbox oil, in degrees Celsius. \\ wgen\_avg\_RrtSpd\_WP2035 & Speed of the rotor main shaft before gearbox, \\  & in revolutions per minute (RPM) \\ wnac\_avg\_WSpd1 & Wind speed in m/s measured by the anemometer \\  & at the wind turbine’s nacelle \\ wrtm\_avg\_TrmTmp\_GbxOil - wnac\_avg\_ExITmp & Difference between the temperature of \\  & the gearbox oil and the external temperature \\ wrtm\_avg\_TrmTmp\_GbxBrg151/wrtm\_avg\_TrmTmp\_GbxOil & Ratio between the speed of the rotor main \\  & shaft before gearbox in the point 151 divided by \\  & the temperature of the gearbox oil. \\ \hline
**Target Name** & **Description** \\ \hline wrtm\_avg\_TrmTmp\_GbxBrg152 & Average temperature of the gearbox bearing 152, \\  & at the high speed shaft (output) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The variables selected by an expert as being relevant for the gearbox operation. Note that to avoid a linear combination of the variables provided by the SCADA system, three variables are artificially generated. \begin{table}
\begin{tabular}{l l} \hline \hline
**GearBox System Model** & **Reference** \\ \hline Mutual Information Feature Selection (MIFS) & [PERSON] [26] \\ Conditional Mutual Information (CMI) & [PERSON] et al. [27] \\ Joint Mutual Information (JMI) & Yang and Moody [28] \\ Min-Redundancy Max-Relevance (mRMR) & [PERSON] et al. [29] \\ Double Input Symmetrical Relevance (DISR) & Meyer and Bontempi [30] \\ Conditional Mutual Info Maximisation (CMIM) & [PERSON] [31] \\ Interaction Capping (ICAP) & [PERSON] [32] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Feature selection algorithms used in Reference [23] to identify the relevant variables, as an alternative to expert-based variable-selection. The regression plots allow us to monitor the performances of the turbines over long periods of time, providing an overview of the model's behaviour in relation to the different turbines in the plant. In the case shown, the period is of two and a half years, which corresponds to the time of the second half of the dataset. The first half has been used to build the model in the training step. During the time interval used for training, the turbines work properly, although repairs or changes of components have been made at some point. Even when including data corresponding to small periods of malfunction, the model ends up adjusting for the cases of good behaviour. This is because comparatively, the number of good cases is much greater than the number of cases of malfunctioning. Consequently, most of the outputs produced by the turbine fit considerably well with the model outputs. Therefore, most points fall on the 45-degree line. The turbines, however, have continued to record alarms and failures in the gearbox subsystem during the test period. Therefore, when a deterioration in performance occurs, some points start to be further away from the 45-degree line occasionally, and the slope of the line changes. After a repair/replacement of the component, the subsystem once again registers a behaviour in line with the originally trained model. Figure 8: Test of the selected model (\(H=92\)) for the WT#80. (**Left**) the regression plot using all the test data. (**Right**) from a temporal window where the target \(\hat{y}\) (**top**) and the error (**bottom**) can be observed in more detail. Figure 7: (**Left**) the Root-Mean-Square Error (RMSE) generated by a model of size \(H\) during the training step. (**Right**) the RMSE in the testing step for each \(H\). Figure 11: Test of the selected model (\(H=92\)) for the WT#83. On the left, the regression plot using all the test data. On the right, from a temporal window where the target \(\hat{y}\) (**top**) and the error (**bottom**) can be observed in more detail. Figure 10: Test of the selected model (\(H=92\)) for the WT#82.