Wavelet-based denoising of partial discharge signals buried in excessive noise and interference

Abstract

Achieving acceptable levels of sensitivity during on-line and/or on-site partial discharge (PD) measurements still continues to remain a very challenging task, primarily due to strong coupling of external (random, discrete spectral and stochastic pulsive) interferences. Many analog and digital approaches have been proposed for suppressing these interferences, and amongst these, rejection of the pulsive type of interferences is known to be vvry diffcult, if not impossible. The time and frequency characteristics of the pulsive interference being very similar to that of the PD pulses is the main reason posing difficulty in their separation. In this paper, a novel, semi-automatic, and empirical wavelet-based method (using multi-resolution signal analysis) is proposed to recover PD pulses, buried in excessive noise/interference comprising of random, discrete spectral, pulsive, and any combination of these interferences occurring simultaneously and overlapping-int' m e with the PD pulses. A critical assessment of the proposed method is carried out, by processing both simulated and practically acquired PD signals. The results ohtained are also compared with those from the best digital filter (infinite impulse response, IIR and finite impul!re response, FIR) method proposed in literature. From the results it emerges that, the wavelet approach is superior and further, has the unique capability of successfully rejecting all the three kinds of interferences, even when PD signals and one or all interferences occur simultaneously and overlap-in-time.

Non-Intrusive Partial Discharges Investigations on Aeronautic Motors

“…Thus, to be recognized as DP, the detected signal should appear with sufficient recurrence and be large enough to be considered as something other than just random noise.The main sources of noise are divided in four categories classified by the type of interferences produced [25]:…”

Removal of Interferences from Partial Discharge Pulses using Wavelet Transform

“…The signals were acquired in laboratory in a realistic manner [14]. For processing the signals, the mother wavelet was selected after rigorous trials through 'db1' to 'db45' and 'bior 1.1' to 'bior 6.8'.…”

An Improved Methodological Approach for Denoising of Partial Discharge Data by the Wavelet Transform

“…We started from a set of candidate wavelet families already used by other authors [12,13,15,17] such as Daubechies (db), Symlets (sym), Biorthogonal (bio), Coiflets (coif); in particular, by taking into account wavelets of different orders, the performances of a total number of 60 wavelets were compared.…”

“…WPT has also been used recently for UWB impulse radio signal denoising [11], although the authors do not comment on possible limitations and drawback of the adopted technique. Satish and Nazneen [12] used the DWT for extracting PD pulses buried in very high levels of noise and interferences and showed that the results obtained with the proposed wavelet-based denoising technique were superior with respect to those obtained with different digital filtering approaches, such as the FIR (Finite Impulse Response) method and the IIR (Infinite Impulse Response)-notch filter method. In the paper they underlined the importance for best results of an optimal choice of the mother wavelet and of the number of levels for decomposition-reconstruction of the input signal, but they did not deal with such a topic.…”