## 1 Introduction
However, predicting interturn faults poses a more intricate challenge, with the constructed RNN for this task exhibiting a relative absolute error of 17.58%. Artificial neural network (ANN) is employed to improve the precision of the Rogers ratio method in reference [10]. However, it is important to acknowledge that the intricacy of an ANN requires substantial storage and computational resources. To tackle this challenge, an optimization approach is utilized with the aim of maximizing accuracy while minimizing the architectural complexity of the ANN. Consequently, post-optimization, the implemented ANN demonstrated a notable level of accuracy, reaching up to 90.7%. A novel intelligent system utilizing dissolved gas analysis (DGA) with a dual purpose: to address the limitations of conventional methods and to enhance transformer diagnosis efficiency through the application of artificial intelligence techniques. The obtained area under the ROC curve and sensitivity average percentages of 98.78-95.19% (\(p\)-value \(<\) 0.001), respectively, underscore the impressive performance of the proposed system, offering a fresh perspective on DGA analysis is presented in is presented in [11]. In reference [12], a multimodal mutual neural network is introduced for assessing the health of power transformers. The experimental findings demonstrate that the proposed approach achieves a high level of classification accuracy and provides precise health assessments for power transformers. Topics related to the application of artificial neural networks (ANNs) in the analysis of the operating conditions of power transformers and electrical energy systems have currently received attention [13; 14; 15]. In recent years, a series of studies and research have been published in this field, demonstrating the growing interest and relevance of these approaches [16; 17; 18; 19]. In this context, this study presents an innovative approach using a pattern recognition artificial neural network (PRN), aiming to diagnose the operating conditions of power transformers (normal operation, thermal faults, and electrical faults) based on combustible gases (H\({}_{2}\) (hydrogen), CH\({}_{4}\) (methane), C\({}_{2}\)H\({}_{2}\) (acetylene), C\({}_{2}\)H\({}_{4}\) (ethylene), and C\({}_{2}\)H\({}_{6}\) (ethane)) present inside over time of use. The difference between this methodology and existing approaches in the literature lies in its remarkable accuracy, achieving a 98% accuracy rate in classification, with model validation reaching 100% accuracy and with a training time of just 2 s for the network with 5 neurons in the hidden layer and with 10 neurons, just 10 s of processing. In contrast to conventional approaches that are limited to identifying a certain class of transformer failure, this research proposes a framework that allows for systematic testing with different configurations to optimize the performance of artificial neural networks. To put it simply, the method that was developed starts the training program over and again while adjusting the settings until the ideal weights are found and noted. Because the most effective parameters are stored, accuracy and efficiency are maximized throughout every training period. Selecting the best artificial neural networks (ANN) model architecture provides a more reliable methodology for practical applications, including pattern recognition skills that efficiently capture nonlinear interactions between input variables.