Fault Signal Recognition Method of Submarine Cable based on Energy Entropy and GWO-ELM

ZHANG Tao; LIU Hao; ZHANG Peilei; LIU Zheheng; SHI Guangrui; FAN Xiping

doi:10.13756/j.gtxyj.2025.240145

您当前的位置：

首页 >

文章列表页 >

Fault Signal Recognition Method of Submarine Cable based on Energy Entropy and GWO-ELM

更新时间：2025-08-16

- Fault Signal Recognition Method of Submarine Cable based on Energy Entropy and GWO-ELM
- Study on Optical Communications Issue 4, (2025)
- 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：
- 基金信息：
- DOI：10.13756/j.gtxyj.2025.240145
  CLC： TP393
- Received：10 July 2024，
  
  Revised：21 August 2024，
  
  Published：10 August 2025
- 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

蔡希鹏 , 邹常跃 , 彭发喜 , 等 . 超大规模海上风电海陆一体直流输电技术探讨 [J ] . 电网技术 , 2024 , 48 ( 7 ): 2895 - 2901 .

Cai X P , Zou C Y , Peng F X , et al . Discussion on HVDC Transmission Technology for Super-large Scale Offshore Wind Power under Offshore-onshore Integration [J ] . Power System Technology , 2024 , 48 ( 7 ): 2895 - 2901 .

鞠森 , 杨志 , 柳小花 . 光纤复合海缆弯曲与光单元应变关系的有限元分析 [J ] . 光通信研究 , 2016 ( 5 ): 39 - 42 .

Ju S , Yang Z , Liu X H . Finite Element Analysis for Bending Degree of Fiber Composite Submarine Cable by Optical Unit Strain [J ] . Study on Optical Communications , 2016 ( 5 ): 39 - 42 .

张畅生 , 郭旭敏 , 刘龙春 , 等 . 高压直流海缆综合在线监测系统研究 [J ] . 电测与仪表 , 2016 , 53 ( 22 ): 27 - 33 .

Zhang C S , Guo X M , Liu L C , et al . Study on Integrated Online Monitoring System of HVDC Submarine Cable [J ] . Electrical Measurement & Instrumentation , 2016 , 53 ( 22 ): 27 - 33 .

吕安强 , 李永倩 , 李静 , 等 . 光电复合海缆中光纤与导体温度关系的有限元分析方法 [J ] . 电工技术学报 , 2014 , 29 ( 4 ): 91 - 96 .

Lü A Q , Li Y Q , Li J , et al . Finite Element Analysis Method for Temperature Relationship between Conductor and Optical Fiber in Optic-electric Composite Submarine Cable [J ] . Transactions of China Electrotechnical Society , 2014 , 29 ( 4 ): 91 - 96 .

郭家兴 , 钱君霞 , 闫安心 , 等 . 基于VMD和排列熵的海缆振动信号降噪方法 [J ] . 光通信技术 , 2024 , 48 ( 2 ): 84 - 89 .

Guo J X , Qian J X , Yan A X , et al . Noise Reduction Method of Submarine Cable Vibration Signal based on VMD and Permutation Entropy [J ] . Optical Communication Technology , 2024 , 48 ( 2 ): 84 - 89 .

尚秋峰 , 郭家兴 , 黄达 . 基于BS-1DCNN的海缆振动信号识别 [J ] . 智能系统学报 , 2024 , 19 ( 4 ): 874 - 884 .

Shang Q F , Guo J X , Huang D . Submarine Cable Vibration Signal Recognition based on BS-1DCNN [J ] . CAAI Transactions on Intelligent Systems , 2024 , 19 ( 4 ): 874 - 884 .

尚秋峰 , 黄达 , 巩彪 . 基于VMD与AdaBoost-SCN的海缆振动信号识别方法 [J ] . 振动与冲击 , 2023 , 42 ( 19 ): 231 - 239 .

Shang Q F , Huang D , Gong B . Submarine Cable Vibration Signal Identification Method based on VMD and AdaBoost-SCN [J ] . Journal of Vibration and Shock , 2023 , 42 ( 19 ): 231 - 239 .

张慧颖 , 梁誉 , 卢宇希 , 等 . 稀疏训练指纹库融合MMPSO-ELM室内可见光定位 [J ] . 激光技术 , 2022 , 46 ( 6 ): 788 - 795 .

Zhang H Y , Liang Y , Lu Y X , et al . Indoor Visible Light Positioning Using MMPSO-ELM Neural Network based on Sparse Training Fingerprint Database [J ] . Laser Technology , 2022 , 46 ( 6 ): 788 - 795 .

尚秋峰 , 巩彪 , 郑国强 . 锚砸作用下光纤复合海底电缆的振动特性分析 [J ] . 光通信研究 , 2021 ( 5 ): 45 - 50 .

Shang Q F , Gong B , Zheng G Q . Analysis of Vibration Characteristics of Optical Fiber Composite Submarine Cable under Anchoring [J ] . Study on Optical Communications , 2021 ( 5 ): 45 - 50 .

Zhang T , Du A , Li L , et al . Analysis of Three-core Composite Submarine Cable Damage Due to Ship Anchor [J ] . IEEE Access , 2022 , 10 : 93910 - 93920 .

张振鹏 , 蒙绍新 , 胡列翔 , 等 . 锚害对500 kV海底电缆结构层的影响 [J ] . 高电压技术 , 2020 , 46 ( 2 ): 561 - 568 .

Zhang Z P , Meng S X , Hu L X , et al . Influence of Anchor Damage on Structure Layer of 500 kV Submarine Power Cables [J ] . High Voltage Engineering , 2020 , 46 ( 2 ): 561 - 568 .

刘子惠 , 胡凯 , 郑新龙 , 等 . 海底光纤复合缆机械损伤有限元仿真分析综述 [J ] . 光通信研究 , 2023 ( 2 ): 45 - 54 .

Liu Z H , Hu K , Zheng X L , et al . Overview of Finite Element Simulation Analysis for Mechanical Damage of Submarine Optical Fiber Composite Power Cable [J ] . Study on Optical Communications , 2023 ( 2 ): 45 - 54 .

王伟 , 汪文东 , 游鹏 , 等 . 基于VMD-GAF-MCNN的多传感融合海缆裸露状态识别 [J ] . 光通信研究 , 2025 ( 3 ): 240060 .

Wang W , Wang W D , You P , et al . Identification of Exposed Submarine Cable Status based on Optimized VMD-GAF-MCNN for Multi Sensor Fusion [J ] . Study on Optical Communications , 2025 ( 3 ): 240060 .

Aciu A M , Nițu M C , Nicola C I , et al . Determining the Remaining Functional Life of Power Transformers Using Multiple Methods of Diagnosing the Operating Condition based on SVM Classification Algorithms [J ] . Machines , 2024 , 12 ( 1 ): 37 - 71 .

Views

下载量

CSCD

CNKI被引量

Alert me when the article has been cited

提交

Tools

Publicity Resources

Application of Distributed Acoustic Sensing in Anchor Damage Monitoring on Submarine Cables

A Fault Diagnosis Method Forwavelet Packet and Neural Network-Based Submarine Cables

Functions and architecture of integrated test systems for FTTx optical access networks

Technological development and applications of fault diagnosis for ODN networks

Related Author

CHEN Qingqing

LI Songlin

WU Zhengming

XIE Shuhong

MU Zhangjian

WANG Daogen

WU Mingnian

ZHANG Hong

Related Institution

Zhongtian Power Optical Cable Co., Ltd.

Jiangsu Zhongtian Technology Co., Ltd.

Department of Electronic and Communication Engineering,North China Electric Power University

Beijing Research Institute,China Telecom Corporation Limited

Postal code：430205
Technical support is provided by Beijing Founder electronics co., LTD 鄂ICP备2022010197 鄂公网安备 42011202002092号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：222510 Visits Today：28

⁰