Partial discharge signal denoising method based on wavelet adaptive threshold

A wavelet self-adaptive, discharge signal technology, applied in the direction of testing dielectric strength, etc., can solve the problem of signal containing noise, achieve good effect and reduce distortion

Inactive Publication Date: 2014-02-12
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

This method can efficiently remove white noise and reduce the distortion of the original signal, thus m

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  • Partial discharge signal denoising method based on wavelet adaptive threshold
  • Partial discharge signal denoising method based on wavelet adaptive threshold
  • Partial discharge signal denoising method based on wavelet adaptive threshold

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Embodiment

[0048] In this embodiment, a partial discharge signal denoising method based on wavelet adaptive threshold, see figure 1 , including the following steps:

[0049] S1. Input the partial discharge signal to be denoised, see Figure 5 ;

[0050] S2. Perform wavelet multi-scale decomposition on the partial discharge signal to obtain the high-frequency coefficients of each decomposition scale and the low-frequency coefficient of the highest decomposition scale;

[0051] S3. Using a non-negative garrote threshold function and an adaptive threshold selection method based on particle swarm optimization, perform quantization processing on the high-frequency coefficient components obtained in step S2 to remove noise components, and save them as new high-frequency coefficient components; thus, By establishing the generalized cross-validation criterion (GCV), combined with the particle swarm optimization algorithm, the wavelet coefficient threshold adaptive selection is realized without...

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Abstract

The invention discloses a partial discharge signal denoising method based on a wavelet adaptive threshold. The partial discharge signal denoising method based on the wavelet adaptive threshold comprises the following steps of (1) inputting a partial discharge signal to be denoised, (2) carrying out wavelet multi-scale decomposition on the partial discharge signal to obtain high-frequency coefficients of decomposition scales and a low-frequency coefficient of a maximum decomposition scale, (3) using a non-negative garrote threshold function and a adaptive threshold selection method based on particle swarm optimization to carry out quantitative processing on high-frequency coefficient components obtained in the step (2) so as to remove noise components, storing the result to serve as new high-frequency coefficient components, (4) carrying out signal reconstruction through the new high-frequency coefficient components and a low-frequency coefficient component, obtained in the step (2), of the maximum decomposition scale to obtain a partial discharge signal without noise, and (5) outputting the partial discharge signal without the noise. The partial discharge signal denoising method based on the wavelet adaptive threshold achieves wavelet coefficient threshold self-adaptation selection on the premise that any priori knowledge does not exist, and is applicable to various actual partial discharge conditions and good in effect of removing white noise, and the denoised partial discharge signal with higher quality can be obtained.

Description

technical field [0001] The invention relates to a partial discharge signal detection technology of electrical equipment, in particular to a partial discharge signal denoising method based on wavelet self-adaptive threshold. Background technique [0002] On-line detection of partial discharge has become an effective method to evaluate the insulation state of electrical equipment. In the online detection, the electrical equipment is in the live operation state, and the on-site interference is serious; the partial discharge signal generated by the insulation defect is usually very weak, and it is easy to be submerged in the serious background noise. Therefore, the suppression of interference is the key issue of partial discharge on-line detection. Generally speaking, the interference in partial discharge on-line detection can be divided into three categories: periodic narrow-band interference, white noise and random pulse interference. Before suppressing random pulse interfer...

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Application Information

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IPC IPC(8): G01R31/12
Inventor 吴炬卓牛海清罗新
Owner SOUTH CHINA UNIV OF TECH
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