A power quality signal denoising method based on improve wavelet threshold function

Abstract

The invention relates to a power quality signal denoising method based on an improved wavelet threshold function. Firstly, an original power quality signal is collected; the signal is preprocessed, aset of wavelet coefficients are obtained by selecting db3 wavelet basis function to decompose the one-dimensional contaminated power quality analog signal into three wavelet layers; then, the improvedwavelet threshold function and the threshold calculated by the unified threshold method are used to quantize the threshold, and the estimated wavelet coefficients are obtained; finally, the high-frequency wavelet coefficients of the first layer to the third layer and the low-frequency wavelet coefficients of the third layer after the threshold quantization process are used to carry out the wavelet inverse transform, and the signal is reconstructed to obtain the reconstructed signal. The improved wavelet threshold function solves the contradiction between denoising and retaining the local characteristic information of the original power quality signal.

Description

technical field [0001] The invention relates to a power quality signal denoising method, in particular to a power quality signal denoising method based on an improved wavelet threshold function. Background technique [0002] In recent years, with the wide use of various power electronic devices and the continuous increase of nonlinear, impact and fluctuating loads, the resulting power quality problems have attracted more and more attention from the power sector and power users, so how to It is particularly important to detect various types of power quality events accurately, quickly and effectively. [0003] However, in practical applications, the power quality signal obtained by monitoring and data acquisition equipment will introduce random noise due to changes in equipment operation and external conditions; in addition, the power quality signal will also be polluted by noise during measurement and channel transmission data . The presence of noise can worsen the effectiv...

AI Technical Summary

Problems solved by technology

Aiming at the deficiencies of the traditional wavelet soft threshold and hard threshold functions for denoising, many improved threshold functions have been proposed, but all of them have the following problems, such as signal blurring at high frequencies after denoising, large amount of calculation, etc. None of them can solve the problem of effectively denoising while retaining the local feature information of the original signal to the greatest extent.

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