Metodologia para classificação de nível de poluição de isoladores com base na medição da corrente de fuga utilizando inteligência artificial.

Abstract

Insulators are equipment responsible for providing electrical insulation and mechanical support to conductors, and are widely used in the Electric Power Systems. System reliability and continuity depend on these equipment operating correctly. Due to the importance of insulators for the electrical system, studies that develop techniques capable of performing the diagnosis/prognosis of this equipment are necessary. This work presents a technique for classification or diagnosis of insulators based on leakage current parameters. Initially, the database was built, through electrical tests to obtain the leakage current with artificially polluted glass insulators at five different levels: clean, very light, light, moderate and heavy. The leakage current parameters that presented the greatest correlation with pollution were: peak value, RMS value and the fundamental harmonic component. The classification models used to determine the level of pollution were based on artificial intelligence techniques, namely: Support Vector Machines, Kohonen Self-Organizing Maps and Naive Bayes. Performance indicators were used to assess the reliability of each of the techniques as well as perform a comparative analysis between them. The indicators used were: accuracy, precision, recall, f1-score and confusion matrix. It was verified that the classification models presented excellent results, mainly the algorithm based on unsupervised learning, Kohonen Maps. Thus, we obtained a classification model with satisfactory performance that can be used in an insulator monitoring and diagnosis system capable of assisting in decision-making processes related to asset management.