## 1 Introduction
### Background and Motivations  
In [29], reports, several methods for fault detection in WTs, which can be classified into model-based, signal-processing, and data-based approaches. Furthermore, ref. [11] presents a review of the literature on converter faults; the authors include model-based, signal-based, and data-based methods and conclude that the data-based method has a high fault diagnosis capability. Furthermore, in [25], a study is presented for detecting faults in WT converters from SCADA data using CNN; the effectiveness of the research was verified through a comparative study. In addition, ref. [30] is a study detailing the most common WT converter faults, and presents a Transfer Learning (TL)-based method; the results demonstrate the accuracy and efficiency of the TL method in diagnosing WT faults. A strategy using wavelet transform, feature analysis, and a Back Propagation Neural Network (BPNN) to accurately identify open-circuit faults in WTs converters, is presentedin [31]; the results show that the proposed strategy can successfully classify converter faults. In addition, ref. [32] describes a data-based approach to detect WT converter faults using an LSTM; the results show that the proposed method has powerful data processing capabilities and higher diagnostic accuracy. Similarly, early anomaly detection and root cause analysis in WTs using SCADA data are proposed in [33]. For this purpose, a hybrid model using a LSTM-based asymmetric variational autoencoding Gaussian mixture model (LSTM-AVAGMM) is employed. The robustness and competitiveness of the model are demonstrated in two case studies. Moreover, a hybrid DL model combining recurrent neural networks (RNN) and LSTM for WT condition monitoring is presented in [34]. LSTM-AE is employed for data processing and feature extraction in the proposed model. SCADA data and simulated data are then used to provide a complete learning model of the WT behavior. The experimental results unequivocally demonstrate that the proposed model surpasses existing ML algorithms in terms of fault prediction accuracy. Likewise, the approach proposed by [35] is based on a supervised implementation of the variational AE model, which allows for the projection of the WT system in a low-dimensional representation space for early fault prediction. Another similar work is presented in [36], where a deep EA (DAE), enhanced by fault cases, is developed for anomaly detection in WT. With the help of fault cases, the DAE can capture normal operation data patterns and acquire deep embedding features. Experimental results show that the method outperformed current AE-based methods in WT anomaly detection using multiple evaluation metrics. In [37], a technique is presented to monitor the aging of insulated-gate bipolar transistor (IGBT) modules in offshore WT converters using SCADA data and a hybrid AE and attention-based LSTM (AT-LSTM) model. The AT-LSTM model is used to learn from SCADA data and establish a temperature prediction. AE is used to detect anomalies. Experimental results validate the effectiveness of the proposed model. Meanwhile, in a previous study, we determined that the scientific literature on using SCADA data to predict failures in WT converters located in complex terrain is limited [24]. The Villonaco Wind Farm (VWF), located in Ecuador in a mountainous area at approximately 2700 masl, presents a challenging climate and irregular terrain, entailing unique characteristics that distinguish it from other wind farms [38]. These unique conditions highlight the need for advanced fault detection approaches and underlie the motivation for this study. In modern WTs, the converter controls the speed and torque of the generator in addition to the power transfer to the grid [39]. Moreover, the study of the converter is a relevant research topic currently because a failure in this component can cause an unexpected WT shutdown, resulting in a decrease in energy production.