Fault diagnosis of insulators from ultrasound detection using neural networks

Reliability in the electric power system is fundamental to the development of society, for which rapid and accurate methods of fault identification are required. Faults in distribution insulators are hardly visible and the fault behavior is often intermittent, which makes its diagnosis a difficult task. Fault diagnosis with the ultrasound equipment has been used efficiently since this equipment is directional and not influenced by sunlight. However, the interpretation of the signal generated by this equipment requires an experienced operator and they are also susceptible to provide false diagnostics. The use of advanced algorithms to classify electrical system conditions has been proven as a great alternative to automate operator decisions. This article proposes the use of artificial intelligence algorithms such as single-layer and multilayer Perceptron for classification of distribution insulators conditions. The use of artificial neural networks for insulator classification is an innovative subject. Some researchers have already worked on partial discharges however not specifically for fault classification in insulators of distribution networks. The application of this technique can make the inspection of the electrical system automated and, in this way, more accurate and efficient. The results of the analysis showed that the application of signal linearization technique joint with artificial intelligence is a good alternative to locate faults in insulators.