Processamento de sinais de descargas parciais em tempo real com base em wavelets e seleção de coeficientes adaptativa espacialmente

Abstract

On-line partial discharge (PD) analysis is a highly pursued feature due to the economy related to scheduled shutdowns, disassembling and transport. Nevertheless, on-site PD measurements are frequently limited by interferences found in high-voltage facilities, a situation that imposes the continued development of PD signal processing methods. In the last years the wavelet transform (WT) has been recognized as a powerful technique for PD processing due to its capacity to process localized, non-stationary signals. Several authors have reported good results of its use and, more recently, new WT-based approaches have been developed specifically to improve PD processing. This document presents a new technique for the processing of partial discharge signals, based on the wavelet transform and a spatially-adaptive coefficient selection procedure. Spatiallyadaptive selection is an excerption approach that aims to explore the localized processing capabilities of the WT as a way to improve the separation of coefficients related to the signal and noise. This approach frequently allows a better processing for time-localized signals, like the PDs, when compared to traditional, threshold-based techniques. In this work the spatial correlations were characterized by the local modulus maxima propagation theory. Coefficients selection was performed by the characterization of maxima lines shapes and classification by a deterministic rule and a pattern classifier. The procedure relies on the Translation-invariant Wavelet Transform as a way to avoid PD pulse losses and improve the signal reconstructions. The technique was numerically and visually evaluated considering simulated and measured signals, both in laboratory and in the field. In all cases the results showed to be similar to or superior than those obtained with the thresholding techniques reported in the bibliograpy. Real-time signal processing is a particularly important feature for PD analysis due to the high sampling rates required and the large amounts of data needed to perform diagnostics. After development, the procedures were implemented as a real-time system aiming the investigation of algorithm characteristics and evaluation of computational charge and storage demands. The system works as an authonomous unit or a digital filter, which may be attached to commercial PD analysers. After implementation and optimizations, the system was evaluated regarding to the maximum sampling rates achievable and classified according to its possible pass-bands