skip to main content
10.1145/3421766.3421783acmotherconferencesArticle/Chapter ViewAbstractPublication PagesaiamConference Proceedingsconference-collections
research-article

Study on Prediction Model of Magnetic Field Intensity of Submarine Power Cable Based on LSTM

Published: 26 October 2020 Publication History

Abstract

After the submarine power cables are laid, the parameters such as distance between cables and surface of the sea (H), distance between two submarine power cables placed in parallel (d) are constant. It is difficult to study the magnetic field intensity distribution of submarine power cable under different H and d, because the submarine cable can't be placed randomly. In order to solve this problem, firstly the influence of different groups of H and d on the distribution of submarine cable magnetic field intensity is studied, and the magnetic field intensity of submarine cable generated by different groups of H and d are used as training samples and testing samples. Then the training samples are used to construct the submarine power cable magnetic field prediction model based on Long Short Term Memory (LSTM) neural network, and the submarine power cable magnetic field is predicted with the testing samples as the input. Finally, the simulation results show that there is good prediction ability of LSTM prediction model for submarine cable magnetic field data.

References

[1]
Zhao. Xiaoling, Liu. Yao, Wu. Jiawei, Xiao. (2020) Jinyu, Hou. Jinming, Gao. Jinghui, Zhong Lisheng, "Technical and economic demands of HVDC submarine cable technology for Global Energy Interconnection," Global Energy Interconnection, 3(2), 120--127.
[2]
Chang. Hsun-Cheng, Chen. Bang-Fuh. (2019) "Mechanical behavior of submarine cable under coupled tension, torsion and compressive loads," Ocean Engineering, 189(10), 106272.
[3]
Bastien. Taormina, etc. (2018) "A review of potential impacts of submarine power cables on the marine environment: Knowledge gaps, recommendations and future directions," Renewable and Sustainable Energy Reviews, 96, 380--391.
[4]
R Zuo. Mingjiu, Tian. Feng, Qiao. Xiaorui. (2011) "Research of parallel placed submarine cable route detection method," Proceeding of Image Analysis and Signal Processing, IASP.
[5]
Kou. Xin, Yin. Chengqun, Lv. Anqiang, Li. Yongqian. (2015) "Prediction of the state of optical fiber composite submarine cable based on BOTDR monitoring data," Electrical Measurement and Instrumentation, 52(3), 48--53.
[6]
Lü. Anqiang, Liu. Zheng, Yin. Chengqun. (2016) "A Fault Diagnosis Method Forwavelet Packet and Neural Network-Based Submarine Cables," STUDY ON OPTICAL COMMUNICATIONS, 194(2), 26--29.
[7]
Sepp. Hochreiter, Jürgen, Schmidhuber. (1997) "Long Short-Term Memory," Neural Computation, 9(8), 1735--1780.
[8]
Alex. Sherstinsky. (2020) "Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network," Physica D: Nonlinear Phenomena, 404(3), 132306.

Cited By

View all
  • (2022)Marine Drifting Trajectory Prediction Based on LSTM-DNN AlgorithmWireless Communications & Mobile Computing10.1155/2022/70994942022Online publication date: 1-Jan-2022

Index Terms

  1. Study on Prediction Model of Magnetic Field Intensity of Submarine Power Cable Based on LSTM

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    AIAM2020: Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture
    October 2020
    566 pages
    ISBN:9781450375535
    DOI:10.1145/3421766
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 26 October 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. AC magnetic field detection method
    2. LSTM
    3. Parallel placement
    4. Prediction of magnetic field intensity
    5. Submarine power cable

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    AIAM2020

    Acceptance Rates

    AIAM2020 Paper Acceptance Rate 100 of 285 submissions, 35%;
    Overall Acceptance Rate 100 of 285 submissions, 35%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)5
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 25 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2022)Marine Drifting Trajectory Prediction Based on LSTM-DNN AlgorithmWireless Communications & Mobile Computing10.1155/2022/70994942022Online publication date: 1-Jan-2022

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media