Ultrasound and Artificial Intelligence Applied to the Pollution Estimation in Insulations

This paper presents an electrical insulator pollution estimation technique based on the ultrasonic noise emitted by these insulators when connected to energized electrodes. The spectral subband centroid energy vectors (SSCEV) algorithm was employed in the signal processing. This algorithm can be understood as being a spectral compression, capable of selecting the most significant frequency bands. The processed audio, changed into SSCEV, constituted a database which was fed into an artificial neural network (ANN), capable of distinguishing with remarkable precision a SSCEV from a polluted insulator from a SSCEV from a less polluted insulator. Finally, in order to validate the technique in the field, measurement campaigns were performed at the Campina Grande 2 substation, belonging to the São Francisco Hydroelectric Company. During these campaigns, ultrasonic noise from several electrical equipments, exposed to different natural pollution degrees, was obtained, and the processing, based on SSCEV and the artificial neural network, was once again applied. As a result, the success rates of more than 80% were generally obtained by the ANN.