## 2 Materials and Methods
### Hybrid Model LSTM-MA-AE
The LSTM-MA-AE approach has a time per epoch of 13.28 s, while the LSTM-AE approach, without the AM, requires only 7.48 s per epoch. Furthermore, the use of PE in the LSTM-MA-AE approach slightly reduces the training time compared to the version without PE, with times of 13.28 s and 13.40 s per epoch, respectively. This suggests that PE and residual connection help make training slightly faster. These results underscore the importance of balancing model accuracy with training time. Although the attention mechanism may increase training time, it can significantly improve model performance by capturing complex features and retaining crucial information in sequential data. Additionally, the impact of the amount of training data on the overall model performance was evaluated, as shown in Table 6. The model for variable _ambient_temperature_avg_ accurately reconstructs the data even with a reduced dataset, yielding promising results. Figure 11a displays the reconstruction of the IGBT maximum temperature variable. This figure shows an increase in temperature from early May to late June 2018. This trend coincides with a converter failure documented in the maintenance record. This fault may be due to an accelerated degradation of the converter since, during that period, according to the power output log, the WT operated at maximum capacity most of the time. Figure 9: Loss comparison during training and validation with different model hyperparameters. (**a**) Loss comparison with different batch sizes. (**b**) Loss comparison with different sequence lengths. (**c**) Loss comparison with different regularization factors. (**d**) Loss comparison with different number of heads. (**e**) Loss comparison with different number of layers. (**f**) Loss comparison with different dropout values. (**g**) Loss comparison with different hidden layer sizes. (**h**) Loss comparison with different learning rate values. Figure 11: Reconstruction of the IGBT temperature variable. (**a**) True and reconstructed signal. (**b**) Reconstruction error. Figure 10: Comparison of training time between different hyperparameter changes. Figure 11 reveals that the true time series and the model reconstruction reflect this increase in temperature. However, when examining the reconstruction loss (errors) shown in Figure 11b, several anomalies exceeded the threshold established prior to the failure event. Although this reconstruction loss is not significant in absolute terms, it is noteworthy when compared to normal values. ### Fault Prediction

Detecting faults in WT components may depend on anomalies in one or more variables related to the target component. In addition, the presence of indications of faults in SCADA variables may not be evident, especially in electronic components. Therefore, a fault prediction system was developed to more accurately detect and anticipate these anomalies. After implementing the failure prediction system, we obtained Figure 12, demonstrating that the prediction system can provide alerts about potential failures up to two months in advance. Figure 12 also displays two alert events that preceded failures recorded in the maintenance file: one on 10 June, and another on 24 June 2018. These alerts occurred consecutively, indicating the system's effectiveness in identifying impending failures. Figure 13, which is an enlargement of the window shown in Figure 12, shows that the model with the fault prediction system with an additional anomaly sliding window at the end of the stage can alert of the presence of anomalies with an average advance of approximately four months for the failures that occurred on 10 and 24 June 2018, and 2 March 2021. However, it is important to note that the model also alerted of anomalies at the end of November 2018, although there was no failure according to the maintenance record. \begin{table}
\begin{tabular}{c c} \hline
**Percent Data** & **Variable 3 (MAE)** \\ \hline
90\% & 0.35 \\
50\% & 1.45 \\
25\% & 3.62 \\ \hline \end{tabular}
\end{table}