Research on White Noise Suppression of High Voltage Switchgear Partial Discharge Based on CSSA Adaptive Wavelet Algorithm

WANG Jing; PU Siyu; ZHI Li; JIANG Lan

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Research on White Noise Suppression of High Voltage Switchgear Partial Discharge Based on CSSA Adaptive Wavelet Algorithm

更新时间：2025-06-18

- Research on White Noise Suppression of High Voltage Switchgear Partial Discharge Based on CSSA Adaptive Wavelet Algorithm
- High Voltage Apparatus Pages: 1-9(2025)
- 作者机构：
  
  1.三峡大学电气与新能源学院，湖北　宜昌　 443002
  2.国网宜昌供电公司，湖北　宜昌　 443002
- 作者简介：
- 基金信息：
  
  Open Foundation Project of State Key Laboratory for Safety Control and Simulation of Power System and Large Power Genera tion Equipment(SKLD21KM11)
- DOI：
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WANG Jing, PU Siyu, ZHI Li, et al. Research on White Noise Suppression of High Voltage Switchgear Partial Discharge Based on CSSA Adaptive Wavelet Algorithm[J/OL]. High voltage apparatus, 2025, 1-9.
DOI：

WANG Jing, PU Siyu, ZHI Li, et al. Research on White Noise Suppression of High Voltage Switchgear Partial Discharge Based on CSSA Adaptive Wavelet Algorithm[J/OL]. High voltage apparatus, 2025, 1-9. DOI：

摘要

局部放电是影响高压开关柜寿命的重要原因，针对检测高压柜局部放电时受噪严重，且现有去噪方法对于多变噪声的自适应性不足的问题，文中提出了一种基于混沌麻雀搜索算法（chaotic sparrow search algorithm，CSSA）的自适应小波去噪算法。首先，基于低频系数多含有用信号能量的特征，每一层均以低频系数能量占比最大者作为最优基小波对信号进行小波分解；其次，以Tent混沌序列分散初始麻雀种群，提升麻雀搜索算法的全局搜索能力；最后基于自适应阈值范围搜索最优阈值，以相对小波熵为适应度值衡量去噪后信号与噪声信号的相关度，判断算法结果是否最优。将文中算法与软阈值法等其他算法对比以验证去噪效果，对仿真局部放电信号与实测信号的多次去噪结果表明，文中算法的信噪比、均方误差、波形相似度等评价参数较其他方法有所提升，在多变噪声环境下的去噪效果也较为稳定，因此有良好的自适应性与应用价值。

Abstract

Partial discharge (PD) is an important factor affecting the life of high voltage switchgear

aiming at the problem that the noise is seriously affected when detecting partial discharge of switchgear and the existing denoising methods are not adaptive enough to the variable noise

this paper presents an adaptive wavelet denoising algorithm based on chaotic sparrow search algorithm (CSSA). Firstly

based on the characteristic that the low frequency coefficient mostly contains the energy of the useful signal

each layer takes the one with the largest proportion of the low frequency coefficient energy as the optimal basic wavelet to decompose the signal. Secondly

the initial sparrow population is dispersed by Tent chaotic sequence to improve the global search ability of sparrow search algorithm (SSA). Finally

the optimal threshold is searched based on the adaptive threshold range

and the correlation between the denoised signal and the noisy signal is measured with the relative wavelet entropy (RWE) as the fitness value to judge whether the algorithm results are optimal. The proposed algorithm is compared with other algorithms such as soft threshold method to measure the denoising effect. The results of multiple denoising of simulated partial discharge signals and actual signals show that the evaluation parameters of the proposed algorithm

such as SNR

MSE

NCC

etc. are improved compared with other methods

and the denoising effect is stable under variable noise environment. Therefore

it has good adaptability and promising application value.

关键词

Keywords

references

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