Estudo comparativo de métodos de filtragem para medições não-invasivas de descargas parciais em sistemas de alta tensão: abordagens com diferentes filtros clássicos, adaptativo e transformada wavelet.

Abstract

The Partial discharge (PD) phenomene occur in segmented manner in electrical insulation. Over time, these occurrences can evolve to a critical state, resulting in short circuits and significant damage to electrical equipment. PD detection and analysis are essential in the context of preventive maintenance, contributing to high availability rates of Electrical Power Systems (EPS). However, interference from various noise sources, such as other electrical equipment and electromagnetic phenomena, makes PD signal detection challenging. This work presents a comparative study of filtering and signal analysis methods in noninvasive measurements of partial discharges in real high voltage systems, using different classical filters and the Wavelet Transform (WT). The research evaluated the effectiveness of several filtering methods in noise reduction, providing better identification and characterization of PD signals. Classic filters such as Moving Average (MA), Butterworth (BW), Chebsyshev (Type I), SavitzkyGolay (SG) and adaptive filtering such as Least Mean Squares (LSM), in addition to techniques based on multiresolution wavelet decomposition, were implemented and compared to verify metrics such as Signal-to-Noise Ratio (SNR), Cross-Correlation (CC), Root Mean Square Error (RMSE), Kurtosis (K), and others, considering the preservation of the essential characteristics of PD signals. The results obtained demonstrated that filtering techniques are crucial for reducing noise effects, although more classical methods present limited efficiency when compared to those with adaptive capacity. The comparative analysis revealed critical points that, although they demonstrated restricted efficiency, contribute significantly to the improvement of electrical equipment monitoring methods.