ROCHA, P. H. V.; http://lattes.cnpq.br/4263182713137336; ROCHA, Pedro Henrique Venske da.
Abstract:
The glass high voltage insulators are, today, the main equipment used in transmission lines.
Although a trend towards replacing them with polymers, are still predominantly found mainly
in transmission networks for high voltage 69 kV, 230 kV and 500 kV. Located over an
extensive geographical area, the methods and inspection procedures become difficult to
perform. The routines of preventive and predictive inspections performed today are equipped
with enough subjectivity and have many factors that restrict its implementation. Depend
solely on the visual assessment of the technical and specific climatic conditions: night,
minimum source of light, especially the moon, high relative humidity and the use of
binoculars. Other inspection techniques were studied, but have not found reports of their use
in energy companies, for the mentioned tensions. This research presents a method for carrying
out inspection on high voltage insulators. The differential method is the use of set in signal
processing techniques with artificial intelligence, so that the electromagnetic spectrum
radiated by the equipment when in operation, should be interpreted and classified according to
the level of pollution of the insulators. Thus, an objective criterion is inserted in the process,
giving the inspector a more efficient tool that does not require only a subjective assessment.
Artificial neural networks performed the classification of signals after processing these
through the wavelet transform. Measurements in the laboratory and field tests were conducted
to serve as a database for training method. The best results show up hit 96.5% in all
measurements.
