Trabalho de Conclusão de Curso de Graduação
Análise de coeficientes wavelet para classificação de sinais acústicos de descarga parciais
Fecha
2023-01-10Autor
Castro, João Vitor Maccari Brabo
Institución
Resumen
Insulation degradation in substation electrical equipment is a problem that
needs to be identified before the equipment is broken, in order to avoid causing an
unexpected interruption that affects the energy supply. Another important point is to
ensure the safety of employers who work in the operation, inspection and maintenance
of this equipment.
Partial discharges are a major cause of this failure, and there is an increasing
concern to identify in a time effective manner, in order not to compromise the
equipment, in addition to being of great interest the classification of the severity level
of the partial discharge, allowing thus, preventive maintenance.
On the other hand, the acoustic signals present in substations tend to be very
polluted, since it’s a place containing numerous electrical equipment that generate
acoustic noise, signals at high frequencies and magnetic fields that can affect acoustic
measurements. For this reason, it is necessary to carry out signal processing to filter
the important characteristics, that must be analyzed during the identification and
classification of partial discharge.
In this work it was proposed to use the Wavelet transform to perform the signal
decomposition in several levels of approximation, detail and signal energy. With the
support of the literature, the Daubechies family was identified as the most promising to
work with acoustic signals. With the support of the Extra High Voltage Laboratory –
Federal University of Pará, it generated a population of acoustic signals from partial
discharge tests in glass insulators, which formed the database that supported the
studies of this work.
Using an exploratory data analysis in conjunction with principal components
analysis, the results obtained from the Wavelet transform, 75% of signals were
classified correctly, without using machine learning techniques, using a rule for
graphical analysis, which compared the first and second principal components with
each other. The possibility of creating a second rule to classify the rest of the population
was also found, which could increase the effectiveness of the method, however the
population decreases to a point where it affects the validation of the method, requiring
more signals to reach a conclusion about its assertiveness. The coefficients obtained
from the Wavelet transform can be easily modeled to work with machine learning which
can improve the efficiency of the method.