## 1 Introduction  
The socioeconomic development of a country directly influences the in demand for energy, resulting in the expansion of the infrastructure that supports it. This is reflected in the implementation of new substations, transmission, and distribution lines, and improvements to existing electrical networks, in which transformers play a crucial role. Oil power transformers represent assets with high added value, essential for the efficient operation of electrical energy transmission and distribution infrastructure [1]. In the event of faults in these transformers, negative socioeconomic impacts occur, such as fines, legal proceedings, interruption in production, safety concerns, and environmental damage, among others. These impacts affect both energy suppliers and consumers [2]. These elements have significantly impacted research and the development of methodologies with predictive and preventive maintenance approaches, aiming to mitigate the causes and consequences of unscheduled service stops. Therefore, understanding the health condition of the transformer and identifying possible initial failures has become an area of intense interest for both researchers and companies in the electrical sector, as evidenced by [3]. The main objective of this focus is to reinforce security and continuity in energy supply. Faults in transformers can originate from several sources, and, consequently, there are several assays and tests available for their detection. Among the possible causes, thermal and electrical stresses triggered by certain events, which result in the degradation of the insulating system, composed of oil and paper, can be diagnosed in the early stages [3; 4; 5]. The dissolved gas analysis (DGA) can be measured using either the more popular method of extracting periodic samples and analyzing them in a laboratory or on-site using portable analytical equipment or continuous gas monitoring equipment installed in the transformer (high value added). Since significant gas concentration growth over a short period of time is a strong indicator of internal evolution failure, in this last example, the time interval between the periodic analyses varies according to the evolution of the gas concentrations detected between the previous and the new measurements. Under normal circumstances, this is usually reviewed once or twice a year; if failure is suspected, this period is decreased to months, weeks, or days, according to the degree of severity [6; 7]. In this context, several approaches and algorithms have been developed with the support of computational intelligence, seeking to provide an accurate and effective diagnosis of the condition of oil transformers [3; 4; 5]. However, the immediate adoption of these techniques by energy utilities is not yet common. This is due to the deep-rooted traditional use of standardized analytical methods, such as gas ratios, key gas, and relative percentage graphs (triangle, pentagon), among others, which are widely used in the evaluation of transformers. These procedures have encouraged companies to invest in tools aimed at optimizing the performance of power transformers (PT). Artificial neural networks (ANN) stand out as one of these tools. Convolutional neural network (CNN) based approach is presented for classifying six types of discharge faults in power transformers in reference [8]. The results obtained in the experiments demonstrate that the proposed method significantly outperforms conventional algorithms, such as linear and nonlinear support vector machines. In reference [9], the potential of employing deep neural networks to uncover concealed patterns within vibration time series for early-stage prediction of transformer under-excitation, over-excitation, and interturn fault progression is explored. The developed network designed for excitation voltage prediction demonstrates outstanding performance, achieving a relative absolute error of 0.56%.