Study on the Characteristics and Denoising of Low-Current Fault Arc Sound Signals Based on GSWOA-VMD-SE

LIU Kai; WEI Haojie; WU Tian; WANG Ming; YANG Ying; CHEN Kai

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Study on the Characteristics and Denoising of Low-Current Fault Arc Sound Signals Based on GSWOA-VMD-SE

更新时间：2025-10-11

- Study on the Characteristics and Denoising of Low-Current Fault Arc Sound Signals Based on GSWOA-VMD-SE
- Moren Journal (2025)
- 作者机构：
  
  1.中国电力科学研究院有限公司,湖北省武汉市430074
  2.三峡大学电气与新能源学院,湖北省宜昌市443002
  3.国网湖北送变电工程有限公司,湖北省武汉市430000
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(51807110)
- DOI：
  CLC：
- Received：07 November 2024，
  
  Revised：2025-05-13，
  
  Accepted：14 May 2025，
- 稿件说明：
移动端阅览
LIU Kai, WEI Haojie, WU Tian, et al. Study on the Characteristics and Denoising of Low-Current Fault Arc Sound Signals Based on GSWOA-VMD-SE[J/OL]. Moren journal, 2025.
DOI：

LIU Kai, WEI Haojie, WU Tian, et al. Study on the Characteristics and Denoising of Low-Current Fault Arc Sound Signals Based on GSWOA-VMD-SE[J/OL]. Moren journal, 2025. DOI：

摘要

小电流接地配电网故障难以准确识别，而基于电弧声信号检测技术可获取电弧的声信号特征以进行故障识别，但在采集过程中受电磁噪声、环境噪声等因素的干扰，需要对其进行降噪处理。为了解决降噪过程中最优分解参数难以确定、计算效率低及对非线性信号降噪效果差的问题，从而有效滤除噪声，保证信号的质量，提出了一种基于GSWOA-VMD-SE的配网故障电弧声感降噪方法。首先，利用引入惯性权重策略的鲸鱼算法(GSWOA)对变分模态分解(VMD)的分解参数进行寻优，提高了最优参数计算速度及精度；然后，通过VMD对电弧声信号进行自适应分解，有效区分了非线性电弧声信号的各模态分量；最后，结合各分量的频谱系数和样本熵(SE)筛选并滤除噪声分量，并将信号分量进行重构，得到了降噪良好的信号。结果表明，本文方法对于不同噪声含量的仿真信号和不同采集距离的电弧声信号，均能有效提高VMD参数的寻优精度和速度，有效区分原始信号和背景噪声，且能在极大程度上保留电弧声信号的原始特征。

Abstract

Accurate fault identification in small-current grounded distribution networks remains challenging. While arc acoustic signal detection technology can obtain acoustic characteristics for fault identification

the acquired signals are contaminated by electromagnetic interference and environmental noise

requiring denoising processing. To address the difficulties in determining optimal decomposition parameters

low computational efficiency

and poor denoising performance for nonlinear signals during the denoising process

this paper proposes a GSWOA-VMD-SE based denoising method for distribution network fault arc acoustic signals. First

the Global Search Whale Optimization Algorithm (GSWOA) is employed to optimize the decomposition parameters of Variational Mode Decomposition (VMD)

improving both the speed and accuracy of optimal parameter calculation. Then

VMD adaptively decomposes the arc acoustic signal

effectively separating modal components of the nonlinear acoustic signal. Finally

noise components are filtered based on spectral coefficients and Sample Entropy (SE)

and the signal components are reconstructed to obtain well-denoised signals. Results demonstrate that the proposed method effectively improves the precision and speed of VMD parameter optimization for both simulated signals with different noise levels and actual arc acoustic signals collected at varying distances. It successfully distinguishes original signals from background noise while largely preserving the original characteristics of arc acoustic signals.

关键词

Keywords

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刘凯( 1979 —)，男，教授级高级工程师，主要研究方向为输电线路运行维护、带电作业技术。(E⁃mail:liukai3@epri.sgcc.com . cn) .

尉浩杰( 2000 —)，男，硕士研究生，主要研究方向为信号处理，(E⁃mail:961749499@qq . com) 。

吴田( 1983 —)，男，高级工程师，主要研究方向为电网智能化运行和检修技术、电网不停电作业技术及装备(通信作者)(E⁃mail:wutian_ 08 @ 163 . com

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