Corona Effect Detection in Energized Polymeric Insulators Using Machine Learning and Ultrasonic Emissions

The occurrence of corona effect is an important indication that an insulator probably has malfunction. Thus, the corona detection can help to diagnose problems in insulators, which might cause serious damage to the power distribution system, such as the sudden interruption of service. Artificial neural networks (ANN) are machine learning techniques for classifying patterns into categories. This paper evaluated the application of neural networks for corona detection in high voltage polymeric insulators using ultrasonic emissions. To perform the capture of these data, a specific electronic circuit was developed to capture the ultrasonic signals tuned in the 40 kHz. The results permit to infer that neural networks showed high generalizability and looks to be a promising technique for this type of problem, using ultrasonic frequencies as the data source.