## 2 Materials and Methods
### Hybrid Model LSTM-MA-AE

Table 6: Model performance for different dataset percentages. Figure 12: Failure prediction with fixed threshold and limited data. The fixed threshold and an adaptive threshold with EWMA were used to reduce the noise generated by the fixed threshold. A limitation of using a fixed threshold is that it can only capture anomalies when there is a trend in the reconstruction loss. Therefore, in this study, since it is necessary to determine anomalies in the WT operation over its entire operating range, an adaptive threshold with EWMA is used. Figure 14 shows the performance of the adaptive threshold with EWMA applied to each reconstruction error of each variable, which improved fault detection and reduced false positives. Figure 15 shows the final result of the failure prediction system. The model correctly detected the converter failures approximately 2.5 and 4.2 months before they occurred. This demonstrates the proposed model's capability for early fault detection. In addition, as seen in Figure 15, an warning at the end of 2019 corresponds to an FP. According to the maintenance record, this event may be due to a failure in the yaw system, and the model could have captured these deviations. Figure 14: EWMA adaptive threshold. Figure 13: Results of the failure prediction system without EWMA for WT2. Parameters used: Threshold\({}_{fixed}\) of each variable corresponds to the 98 th percentile of the data; N\({}_{windon1}\): 30 days; N\({}_{anomalies1}\): 30. The study was also conducted for WT3 of the VWF to determine the effectiveness of the proposed model. Figure 16 validates this failure prediction method because the model detected anomalies approximately 2.3, 3.7, and 4.3 months in advance of the failures, which occurred on 25 May 2019, 23 December 2019, and 31 March 2021, respectively. The metrics described in Section 2.15 were used to evaluate the model's performance. Table 7 shows that the model is able to anticipate failures an average of 3.39 months in advance for both WTs. The advance to fault (Adv.) is expressed in months. Finally, we have developed a comparison with similar recent studies in WT converter failure prediction. Although direct comparisons are limited due to variability in the \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**WT** & **TP** & **FP** & **FN** & **Re** & **Pr** & **F1** & **Adv. 1** & **Adv. 2** & **Adv. 3** & **Adv. Avg.** \\ \hline WT2 & 2 & 1 & 0 & 1.00 & 0.67 & 0.80 & 2.50 & - & 4.20 & 3.35 \\ WT3 & 3 & 0 & 0 & 1.00 & 1.00 & 1.00 & 2.30 & 3.70 & 4.30 & 3.43 \\  & & & & & & & & & Avg. & 3.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Fault prediction system performance metrics. Figure 16: Results of the failure prediction system for WT3. Parameters used: \(\alpha\): 0.32; \(\lambda\): 1.2; N\({}_{window1}\): 30 days; N\({}_{anomalies1}\): 50. Figure 15: Results of the failure prediction system for WT2. Parameters used: \(\alpha\): 0.3; \(\lambda\): 1; N\({}_{window1}\): 15 days; N\({}_{anomalies1}\): 30. The June 24, 2018 failure (purple) could be a consequence of the failure preceding it, which not being due to degradation and the short time between the two failures (14 days), the model is not able to generate a warning. datasets used in each study, this comparison provides an overview of our model's relative performance in relation to previous work. In Table 8, an impact matrix summarizing the contributions of each study is presented. ## 4 Conclusions

Since electronic converters are critical components, the ability to predict failures months in advance is crucial to reducing O&M costs. Therefore, this study addresses the prediction of converter failures in WTs located in complex terrain. We developed a hybrid model using SCADA data that combines LSTM, Multi-head Attention, and Autoencoder, using their strengths to learn temporal and spatial characteristics between variables. The LSTM provides better generalization, while the Multi-head Attention mechanism captures complex patterns inherent in the data.