Wavelet base selection for de-noising and extraction of partial discharge pulses in noisy environment


Wavelet-based de-noising is used to separate partial discharge (PD) signals from the noises ensuing from measurement circuits or the encircling surroundings. PD de-noising by using the wavelet shrinkage methodology is capable of separating the noise element to some extent, however the selection of the wavelet base contains a remarkable result on the de-noising results. The wavelet base is directly related to the distortion of the PD waveform and quality of the de-noising process. Although there are applications on PD noise separation within the literature, the choice of the wavelet base, that affects the evaluation of the PD characteristics, is still challenging. Rather than using correlation-based mostly wavelet base choice for de-noising PD data, during this study a unique wavelet base selection method primarily based on the foremost informative sub-band energy and entropy for separating noise from PD pulses is introduced and successfully applied to raw data obtained from the PD measurement set-up. The advantage of the proposed methodology is that the wavelet base choice answer is automatic and independent of the original noise-free pulse waveform. This study shows that the proposed technique is useful for the extraction of noisy PD pulses by describing the essential discharge parameters like discharge amplitude and also the period and time of incidence more clearly.

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