## 1 Introduction  
The world is currently facing the depletion of traditional energy resources such as oil and coal, and the challenges of climate change. At the same time, global energy demand continues to grow to meet economic and social needs. In response, clean energy is emerging as an alternative solution, releasing less carbon dioxide and offering unlimited energy sources to drive sustainable development [1]. The initiative to achieve carbon neutrality by 2050, proposed in the Paris Agreement, has increased interest and investment in the renewable energy sector. Wind energy, in particular, represents a viable alternative to fossil fuel power generation, using natural sources to produce electricity. The cost of producing energy with this technology is decreasing yearly, with this trend being expected to continue [2]. Moreover, the continued development of wind turbines (WTs), driven by the integration of advanced technologies, is expected to significantly increase their efficiency and generating capacity. However, wind power generation faces challenges regarding the availability, reliability, and lifetime of WTs. For this reason, manufacturers have focused their efforts on prolonging the lifetime of electrical and mechanical systems, which has resulted in reduced failures during operation and consistent production of high-quality power [3]. Unexpected failures in these systems can negatively affect availability and production rate. Components such as blades [4], generators [5], power converters [3], and gearboxes [6] are especially vulnerable to failure due to harsh environmental and operating conditions, leading to extended downtime for maintenance. If unaddressed, these challenges could significantly impact the renewable energy industry. Fault detection is a critical problem with two main aspects: accuracy and cost [7]. The high vibration and noise levels of generators complicate the accurate measurement of faults. In addition, the diversity of faults depending on environmental conditions requires an accurate classification of each situation [8]. Implementing multiple sensors and complex algorithms is costly, especially for large-scale wind farms, and challenging to apply in smaller installations. As WTs increase in size and number, the need for cost reduction in monitoring and repair becomes more urgent [9; 10]. One of the main WT components is the converter. The converters in WTs play a crucial role in facilitating the control and efficient use of energy, especially in complex terrain and with lower maintenance requirements [11]. The converter is a critical component in the operation of the WT, as it converts the variable frequency and voltage output from the generator into a stable, grid-compatible alternating current (AC) with a consistent frequency and voltage, ensuring that the electrical power generated is suitable for transmission and use [12]. According to [13; 14], converters are one of the components that fail most in WTs, responsible for approximately 23% of the failures; the authors indicate that humidity, contamination, and other factors play an essential role in the incidence of these failures. Additionally, with the increasing development of direct-drive WTs, the converter is one of the most failure-prone components. This aspect becomes especially critical in complex terrains, where environmental and operating conditions are significantly more challenging than in less demanding terrains [15]. This study proposes an improved approach to fault detection using Supervisory Control and Data Acquisition (SCADA) system data. The goal is to detect faults in the converter of a WT located in complex terrain. This approach aims to optimize reliability and reduce the costs associated with operation and maintenance (O&M). The ultimate objective is to ensure the greater long-term viability and sustainability of wind energy. Two common approaches to detecting WT faults are classification algorithms (supervised) and reconstruction models (unsupervised) [16].