Classificação binária do estado operacional de transformadores de potência utilizando Índice de Desempenho e Modelos de Aprendizado de Máquina

Power transformers are strategic and valuable assets in electrical systems, as their unexpected failures can lead to significant operational and financial losses for power sector companies and consumers. Although there have been advancements in monitoring their operational conditions, some methodologies still require specialized interpretation, lack standardization, or adopt models whose complexity can hinder integration with the usual operational practices of maintenance professionals. In this context, the objective of this work is to develop binary classifiers based on machine learning algorithms for fast and efficient prediction of the operational state of power transformers, labeled as Satisfactory or Unsatisfactory, using data derived from physicochemical tests, Dissolved Gas Analysis (DGA), and performance indices, based on real equipment samples. The methodology involves the development of supervised machine learning models, such as Random Forest, HistGradientBoosting, Balanced Logistic Regression, and XGBoost, implemented with stratified cross-validation. The results indicate that the classifiers can satisfactorily identify transformers in critical condition, even in a scenario with considerable data dispersion. Therefore, the proposed approach represents a promising tool for technical decision-making in preventive maintenance strategies, combining reliability, scalability, and ease of application in field environments.