Abstract
Partial Discharge (PD) measurements of large generators are extremely affected and hampered by noise, making the denoising of PD signal an inevitable issue. Wavelet shrinkage is the most commonly employed method for PD signal denoising. The appropriate mother wavelet and decomposition level are critically important for the denoising performance. In consideration of the PD signal characteristics of large generators, a novel wavelet shrinkage scheme for PD signal denoising is presented. In the scheme, a scale dependent wavelet selection method is proposed; the core idea is that the optimum wavelet at each scale is selected as the one maximizing the energy ratio of coefficients beside and inside the range formed by the threshold, which correspond to the signal to be reserved and noise to be removed, respectively. In addition, taking into account the influence of mother wavelet at each scale on the decomposition level, an approach for decomposition level determination is put forward based on the energy composition after decomposition at each scale. The application results on the simulated signals with different SNR obtained by combining the various pulses and measured signal on-site show the effectiveness of the proposed scheme. Besides, the denoising results are compared with that of the existing wavelet selection methods and the proposed wavelet selection method shows an obvious advantage.
Highlights
Partial discharge (PD) measurements have been made on electrical equipment insulation for many years and proved to be a sensitive means of insulation condition assessment and fault diagnosis [1,2,3,4,5,6,7].electrical equipment operates in a strong electromagnetic environment, and PD on-line measurements are extremely affected and hampered by electrical noise [2]
The optimum wavelet is selected as the one maximizing the energy ratio of coefficients beside and inside the range formed by the threshold, which represent the signal to be reserved and noise to be removed, respectively
It is worth mentioning that the proposed method for optimum wavelet selection is more time-consuming than the other energy based wavelet selection methods; the extra time is mainly spent on the threshold estimation for each candidate mother wavelet
Summary
Partial discharge (PD) measurements have been made on electrical equipment insulation for many years and proved to be a sensitive means of insulation condition assessment and fault diagnosis [1,2,3,4,5,6,7]. A too large decomposition level will increase calculation complexity without significant improvement in the denoising effect; a too small decomposition level cannot achieve desirable denoising results In previous studies, it was determined based on prior knowledge about signal energy distribution in the wavelet domain [6] or through trial and error [9,10,11], which is expert-dependent and subjective. (2) the decomposition wavelets at the all SNR scalesofinstead of the frequency analysis before the wavelet (3) the level is determined considering thethe influence of thePD optimum wavelets at allsignals scales instead of proposed scheme is applied to simulated signalsmother and measured. Simulated PD signals and measured PD signals of large generators, obtaining outstanding results
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