Estimação inteligente da poluição de isolamentos elétricos baseada nos VECSE do ruído ultrassônico.

Abstract

This work presents an electrical insulator pollution estimation technique based on the ultrasonic noise emitted by them, when connected to energized electrodes. In order to attain this objective, laboratory tests were performed, during which the ultrasonic noise was digitally registered for further study of the best processing and attribute extraction method. As a consequence of this study, the Spectral Sub-band Centroid Energy Vectors (SSCEV) algorithm was obtained, which can be understood as a spectral compression, capable of selecting the most significant frequency bands of the noise. Afterwards, the processed audio, changed into SSCEV, constituted a database which was fed to an Artificial Neural Network, 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 in the substation Campina Grande II, of 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 Artificial Neural Network was once again applied. As a result, success rates of over 80% were generally obtained by the Artificial Neural Network.