Monitoramento e diagnóstico de isoladores poliméricos utilizando processamento digital de imagens termográficas.

Abstract

The objective of this work is to propose a polymeric insulators classification technique that allows the recommendation of its state of degradation in operation, in order to determine the most appropriate time to perform maintenance (exchanges), thus contributing to the reduction of interruptions . The technique relies on analysis of the infrared radiation (IF) emitted by the tested insulators. Polymeric insulators 230 kV were used with varying levels of degradation as test objects. The IF images obtained during the test were subjected to image processing using the RGB type image in order to minimize the existing noise. A neural network to classify the state of degradation of polymeric insulators was proposed. The network entries are the variations in temperature in the body of the insulator and temperature obtained from the measurement of the RIF. The network output was the degree of practicality the state of degradation of the insulators. The results show the technical efficiency in aid to decision-making, the need to replace or not the insulators. It is emphasized that the isolation technique of measuring infrared radiation, thermal imaging is not sufficient for the diagnosis of insulators, however it is essential to diagnose faults on polymeric insulators.