# Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines
## 3 Results
### Experiments for the Gearbox  
Now, a model of the turbine gearbox is analyzed. The main reason for choosing the gearbox is because it is an expensive subsystem with failures that take a long time to repair. According to Reference [25], the gearbox is the subsystem with the highest cost per failure, on average. In addition, it is an expensive process because it requires special equipment such as cranes. Because the replacement failure rate of the gearbox is high and the time needed to repair it is also notably noticeable, the costs derived from the damages of its components represent a high portion of the maintenance costs of wind plants. Therefore, having information about the deterioration of the system's operation is one of the objectives that can most directly affect the reduction of maintenance and operating costs of the wind plant. The variables summarized in Table 2 are used to build the model. The set of these variables can be determined, as in our case, by an experienced expert who knows the physics of the system. These variables could also be obtained by feature selection algorithms. A list of feature selection algorithms reported in the literature, used in the context of this experiment, is presented in Table 3. However, according to previous works, the variables selected by an expert give excellent results [23], and this is in fact the method used in this study. The goal is to build a model to predict the temperature in one of the axes that turns faster by using different variables measured in other points of the same system and external parameters such as ambient temperature or wind speed. Figure 6: Regression plots of the plant model for turbines WT#80 to WT#83, using the SCADA data generated during one week for the case of \(H=43\). A gearbox wind plant model is built using the SCADA data aggregated in one-hour periods. Again, the records with missing data are deleted, following the same criteria of the previous experiments. Then, the first half of all available data from each of the turbines is selected for training SLFNs of size from \(H=1\) to \(H=150\). Each one of these networks is tested with the other half of the data. The results, in terms of the RMSE, are presented in Figure 7, on the left side. For each one of the networks with different \(H\), the parameters **W**, **B** and **b** are stored. With this first analysis, the behaviour of the model and its degree of improvement depending on the size of the network, \(H\), can be seen. To determine the optimal size, the models are evaluated using the test data. The results are shown in Figure 7, on the right side. As shown, the RMSE values obtained are slightly higher than the ones from the training step. Unlike in the previous model, large networks now show a better behaviour. The optimum case is obtained when \(H=92\), and values around or slightly above this would give the models a similar performance. Once the model is selected, it is tested on each of the turbines. In Figures 8-12, the regression plots of the model are presented. On the right-hand side of the figures, the temporal behaviour of the model is depicted, illustrating the degree of accuracy of the model as well as the error generated. The time interval chosen for this representation was selected randomly, but is then the same for each turbine. In these figures, the target, the output provided by the model (\(\vartheta\)) and the error made in the estimation are represented. It can be seen that the turbines follow the model accurately, with the occasional presence of malfunction points. Note that the WT#84 (Figure 12) has more malfunction points than the rest.