# Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines
## 3 Results
### Experiments for the Generator
#### 3.1.1 Experiments at Individual Turbine Level  
In this first experiment, individual WT data is considered. The 5-min data is aggregated into one-hour periods, deleting records with missing data. Then, half of the data is used to train the model and the other half is used to test it. The training step shows that increasing the size of the network leads to a consistent improvement in the performance of the model when evaluated with the same data used for training. In Figure 2, the training performance of networks of size \(H=10\), 20 and 30 is presented in terms of the regression plot. The plot represents the pair drawn by the model output against its associated target. In the ideal case, a 45-degree line would be drawn. The autocorrelation between the estimate and the target, for \(H=10\), 20 and 30, takes the values of \(R=0.99795\), 0.99974 and 0.99989, respectively.  
Note that in the training part, the performance increases with the size of the network. As is usual in ELM training (or in general in any type of system with parameters to be tuned), there is a tendency to improve results in the training phase when the size of the network increases. If it increases indefinitely, the network could learn all the training data and work correctly in the training set, but generating an overfitting. As the essential point is that the network works well when used with new data, that is, with data that has not been used for training, the H parameter must be adjusted to the optimal value that collects the essential characteristics of the data. As the essential point is that the network works well when used with new data, that is, with data that has not been used for training, the H parameter must be adjusted to the optimal value that collects the essential characteristics  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Generator System Model** & \\ \hline
**Variable Name** & **Description** \\ \hline wgen\_avg\_GnTmp\_phsB & Average temperature of the generator’s winding, phase B \\ wgen\_avg\_GnTmp\_phsC & Average temperature of the generator’s winding, phase C \\ wgen\_avg\_RtrSpd\_IGR & Average speed of the rotor at the inductive sensor \\ wgen\_avg\_Spd & Average speed of the generator \\ wgen\_avg\_RtrSpd\_WP2035 & Average speed of the rotor at the mita-teknik WP2035 monitor \\ \hline
**Target Name** & **Description** \\ \hline wgen\_avg\_GnTmp\_phsA & Average temperature of the generator’s winding, phase A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the variables and target used for the generator model.  
of the data. The optimum value of H is found using the network in the test data, where performance improves as H increases to a point where it becomes saturated and begins to worsen due to the overfitting effect. A significant advantage of ELMs is that training the models is a very fast process. Therefore the identification of the optimal H parameter is done in a short time.  
Figure 3 shows the performance of networks of size \(H=15\), 20, 25, 30, 35 and 40 nodes in terms of the regression plot, but now evaluated with the testing data. Although the results of the autocorrelation are still very good, a slight decrease is observed when the size for this model is not close to the optimal one, which experimentally has been found to be \(H=35\).  
Figure 3: Regression plots of the individual model for the WT#80 in the test step. Values around \(H=35\) obtain optimal results.  
Figure 2: Regression plots of the individual model for the WT#80 in the training step as the size of the network increases (\(H=10\), 20 and 30).  
It should also be noted that the network has been evaluated with half of the available data. However, the test can be performed using shorter signal periods, which can be in the order of months, weeks or days (see Figure 4 as an example).