An adaptive denoising method for partial discharge signals based on spectral kurtosis and s-transform

Abstract

The invention discloses a self-adaptive denoising method for partial discharge signals on the basis of spectral kurtosis and S transformation. The self-adaptive denoising method comprises the following steps of: through a discrete signal rapid spectral-kurtosis diagram and S transformation time-frequency spectrum calculation, rapidly acquiring time-frequency characteristics of the partial discharge signals; by combination of the time-frequency characteristics of the partial discharge signals, optimizing value ranges of all parameters in a partial-discharge-signal matching atom dictionary in a self-adaptive manner; then finding atoms with maximum inner product with noised partial discharge signals as optimum matching atoms from the optimized results of the partial-discharge-signal matching atom dictionary by an MP (Matching Pursuit) algorithm; finally, on the basis of the optimum matching atoms obtained by all iterations, carrying out sparse representation on the original partial discharge pulse signals so as to achieve the purpose of denoising the partial discharge signals. The self-adaptive denoising method disclosed by the invention has the advantages that the accuracy of the denoising result is high, no waveform distortion is caused, no limitation on the number of measured local discharge pulses and the positions of the pulses is formed, and the application is wide.

Description

technical field [0001] The invention relates to the technical field of partial discharge, in particular to a method for denoising a partial discharge signal. Background technique [0002] Partial discharge detection is one of the common methods for evaluating the insulation performance of high voltage power equipment. However, due to the existence of background noise, the signals collected by online partial discharge detection often contain a lot of noise, which brings certain difficulties to the analysis and identification of partial discharge signal characteristics in the later stage. Therefore, noise suppression has now become a key technology for on-line partial discharge monitoring. [0003] There are many denoising methods for partial discharge signals. At present, the commonly used partial discharge signal denoising methods include wavelet denoising method, empirical mode decomposition (empirical mode decomposition, EMD) denoising method, Fourier transform denoising ...

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Problems solved by technology

[0008] The purpose of the present invention is to overcome the problems of large amount of calculation, signal distortion and incomplete denoising encountered in the process of partial discharge signal denoising in the prior art

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