基于能量熵与GWO-ELM的海缆故障信号识别方法

张涛; 刘昊; 张培蕾; 刘哲恒; 时光蕤; 范希评

doi:10.13756/j.gtxyj.2025.240145

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基于能量熵与GWO-ELM的海缆故障信号识别方法

专题：海洋通信与传感网络 | 更新时间：2025-08-16

- 基于能量熵与GWO-ELM的海缆故障信号识别方法
- Fault Signal Recognition Method of Submarine Cable based on Energy Entropy and GWO-ELM
- 光通信研究 2025年第4期
- 作者机构：
  
  1.三峡大学　电气与新能源学院，湖北　宜昌　443002
  2.湖北省输电线路工程技术研究中心，湖北　宜昌　443002
  3.华润电力湖北有限公司，湖北　赤壁　437300
- 作者简介：
  
  张涛（1981-），男，安徽阜阳人。教授，博士，主要研究方向为高电压绝缘及测试技术。
  刘昊，硕士。E-mail：479117340@qq.com
- 基金信息：
- DOI：10.13756/j.gtxyj.2025.240145
  中图分类号： TP393
- 收稿日期：2024-07-10，
  
  修回日期：2024-08-21，
  
  纸质出版日期：2025-08-10
- 稿件说明：
移动端阅览
张涛，刘昊，张培蕾，等. 基于能量熵与GWO-ELM的海缆故障信号识别方法[J]. 光通信研究，2025(4): 240145.

Zhang T, Liu H, Zhang P L, et al. Fault Signal Recognition Method of Submarine Cable based on Energy Entropy and GWO-ELM[J]. Study on Optical Communications, 2025(4): 240145.
张涛，刘昊，张培蕾，等. 基于能量熵与GWO-ELM的海缆故障信号识别方法[J]. 光通信研究，2025(4): 240145. DOI： 10.13756/j.gtxyj.2025.240145.

Zhang T, Liu H, Zhang P L, et al. Fault Signal Recognition Method of Submarine Cable based on Energy Entropy and GWO-ELM[J]. Study on Optical Communications, 2025(4): 240145. DOI： 10.13756/j.gtxyj.2025.240145.

摘要

【目的】

针对海底电缆振动信号研究中特征提取效果不佳以及故障诊断效率较低的问题，文章提出了一种基于能量熵特征和灰狼优化（GWO）算法优化极限学习机（ELM）的海缆故障诊断方法。

【方法】

首先，采用有限元仿真软件模拟得出不同工况的海缆光单元振动速度信号；然后，结合经验模态分解（EMD）算法将振动信号分解为4个含有固有特征的本征模函数（IMF）分量，提取计算各个IMF分量的能量熵，将其作为指标构建特征向量；最后，将不同工况下的特征向量样本输入采用GWO-ELM方法的分类器中进行工作状态的判断。

【结果】

在足量样本以及多次计算求平均值的情况下，通过数据总结得出该方法对海缆不同状态的识别准确率能够达到97.4%，分类算法识别时间低至0.968 5 s，并设置了多个算法对比组，结果表明，文章所提方法在提取信号特征以及故障诊断方面有着良好效果。

【结论】

目前海缆在线监测常采用温度和应力分析，文章所提方法在大大降低测量难度的同时，故障特征提取效果较好，能够准确识别海缆的实时工作状态。

Abstract

【Objective】

Aiming at the problems of poor feature extraction and low fault diagnosis efficiency in the study of submarine cable vibration signals

a fault diagnosis method for submarine cable is presented based on the combination of energy entropy features

Grey Wolf Optimizer (GWO) algorithm and Extreme Learning Machine (ELM).

【Methods】

Firstly

the finite element simulation software is used to simulate the vibration velocity signals of submarine cable optical unit under different working conditions. Secondly

the vibration signal is decomposed into four Intrinsic Mode Functions (IMF) using the Empirical Mode Decomposition (EMD) algorithm. These IMFs capture the inherent characteristics of the signal. Subsequently

the energy entropy of each IMF component is extracted and calculated. The energy entropy is used as an indicator to construct the eigenvectors. Finally

the eigenvector samples under different working conditions are input into the classifier using GWO-ELM method to judge the operation state.

【Results】

With a sufficient number of samples and multiple calculations for averaging

it is concluded that the accuracy of this method for recognizing different conditions of submarine cable can reach 97.4%. The recognition time is as low as 0.968 5 s

and multiple comparison groups with different algorithms have been established. The results show that the proposed method has good performance in extracting signal features and fault diagnosis.

【Conclusion】

The method proposed in the paper can accurately identify the real-time working status with better fault feature extraction while significantly reducing the difficulty of measurement.

关键词

Keywords

references

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