## 2 Materials and Methods
### Hybrid Model LSTM-MA-AE
The Autoencoder mechanism allows the model to reconstruct features with spatial and temporal information, facilitating the detection of malfunctions in variables related to the target component. The LSTM-MA-AE model can predict failures an average of approximately 3.3 months in advance and with an average F1 of 90% in the evaluated WTs, showing a low false positive rate. These medium-term failures could be indicative of converter degradation. This method can facilitate early fault detection and provides a robust mechanism for predictive maintenance in wind farms, ensuring greater reliability and generation efficiency. Moreover, we are confident that the methodological process we have developed could be successfully replicated in other wind farms. In fact, we believe it could even be applied to other components of the WT, not just the converter, further extending the reach of our research. As a future work, it is proposed to use the parallelization capabilities of the Transformer model and the strengths of the self-attention mechanism to determine the variables that are intrinsically related to the component failure and to determine the root cause. [PERSON] and [PERSON] contributed to the conceptualization and methodological design of the proposal; were responsible for data preparation and analysis; [PERSON] and [PERSON] prepared the original draft; [PERSON], [PERSON] and [PERSON] contributed to the review and editing, and [PERSON] was responsible for project supervision. All authors have read and agreed to the published version of the manuscript. This research was partially funded by the Junta de Comunidades de Castilla-La Mancha and the E.U. FEDER (SBPLY/23/180225/000226) and by the Spanish Ministry of Economy and Competitiveness and the European Union (PID2021-126082 OB-C21). Not applicable. The datasets presented in this article are not readily available because there is a confidentiality agreement between the owners and the authors. Requests for access to the datasets should be directed to the corresponding author. The authors would like to recognize the assistance received by National University of Loja through the research project 20-DI-FEIRNNR-2023. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Criterion** & [37] & [24] & **Our Work** \\ \hline Fault prediction (F1) & - & **1.00** & 0.90 \\ Early fault detection & Medium-High & **High** & Medium-High \\ Computational efficiency & **High** & **High** & **High** \\ Use of Multi-Head Attention & No & **Yes** & **Yes** \\ Application in complex terrains & No & **Yes** & **Yes** \\ Advance & 4.32 months & 4 months & **3.39 months** \\ Limit to failure & 3 months & 3 months & **2.30 months** \\ \hline \hline \end{tabular} Note: In bold are the best performance.

\end{table} 
Table 8: Impact matrix comparing this study with previous studies. ## Conflicts of Interest: The authors declare no conflicts of interest. ## References

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