MACÊDO, E. C. T.; http://lattes.cnpq.br/1567664152355721; MACÊDO, Euler Cássio Tavares de.
Resumen:
Insulation failure in power systems plant may be caused by manufacturing defects or by electrical,
mechanical, thermal and chemical process which occur during operation. These processes
create defects, including voids in solids, which locally reduces the dielectric insulation strength.
Electrical discharge, may occur in these regions of reduced dielectric strength without discharge
occurring in the regions of non-degraded insulation. A category of this electrical discharges is
labeled Partial Discharges. The Partial Discharges measurement has long been used to evaluate
insulation system design and as a quality assurance test for High Voltage apparatus prior to
installation and during the equipment operational time. Partial Discharge is characterized by
high frequency current pulses originating in gas ionization processes when damaged insulation
is submitted to high values of electric field. The aim of this thesis is to present a comprehensive
methodology for emulation, processing, and automatic classification of several types of partial
discharge signals. For the generation of partial discharges in laboratory it was developed a
hermetic cell that allowed the generation of well defined partial discharges signals. It was verified
during the measurements that the partial discharges signal were susceptible to noise existing
in measurement area. To mitigate this problem, it was used the Wavelet Transform. Several
partial discharges signals obtained in laboratory were evaluated and was verified a significat
noise level reduction after the filtering process. Among the evaluated wavelets families, the
Daubechies have presented the best performance, besides a lower computational processing
time in relation to the other wavelet families. In the sequence, feature extraction was performed
using statistical parameters calculation for each insulation defect configuration. The obtained
feature data was used as input in a variety of Artificial Neural Network (ANN) topologies for an
automatic identification and interactive determination of the most suitable ANN topology (i.e.
number of artificial neurons and hidden layers) for this purpose. Three topologies of ANN were
implemented, the first was based in one hidden layes, the second in two hidden layes and the
last in tree hidden layers. The performance of classification was satisfatory, mainly the topology
based on two hidden layers. It was obtained a global recognition rate of aproximatedely 96%,
presenting a better result in comparison with the other topologies. The performance of the ANN
topologies was evaluated using the mean squared error and confusion plots.