Diagnóstico de falhas em transformadores de potência pela análise de gases dissolvidos em óleo isolante através de redes neurais

Abstract

Substation Equipments that use insulation oil for isolate their internal parts needs a periodic maintenance program to detect possible fails like cellulose deterioration of coils insulation, short circuit between their springs, dissolved gas in oil caused by its deterioration, etc. Actually, preventive maintenance programs have been used by generation, transmission and distribution companies, as a fundamental tool to identify incipient faults, trying to avoid that these faults come to take away the equipment from electrical system, carrying out great financial losses caused by decreasing invoicing, payment of fines to regulatory agency or by decreasing of system reliability. Trying to avoid these inconvenient, on-line sensors and intelligent artificial (IA) techniques has been found application on electrical system engineering. This dissertation is a study of one of these techniques – gas chromatography associated with neural networks – looking to support presents and futures fault diagnosis based on results from chromatography by the analysis of dissolved insulation oil gases during the useful power transformer life, avoiding this way the inconvenient related above, making easy the decision of engineers and technicians about the predictive maintenance of these equipment and also serving as a base for the on-line sensors actuation diagnosis if installed on these allowing yet an estimated old age degree and so the useful age of transformer. Techniques like that from this study may be associated with other IA tools like fuzzy logic, genetic algorithms, expert system and others, consisting the system called hybrid, attempted to get the best solution for the problem.