IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i24p4774-d298024.html
   My bibliography  Save this article

Design of Partial Discharge Test Environment for Oil-Filled Submarine Cable Terminals and Ultrasonic Monitoring

Author

Listed:
  • Yulong Wang

    (Key Laboratory of Engineering Dielectrics and its Application, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China
    College of Rongcheng, Harbin University of Science and Technology, Rongcheng 264300, China)

  • Xiaohong Zhang

    (Key Laboratory of Engineering Dielectrics and its Application, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China)

  • Lili Li

    (Key Laboratory of Engineering Dielectrics and its Application, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China
    College of Rongcheng, Harbin University of Science and Technology, Rongcheng 264300, China)

  • Jinyang Du

    (Key Laboratory of Engineering Dielectrics and its Application, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China)

  • Junguo Gao

    (Key Laboratory of Engineering Dielectrics and its Application, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China)

Abstract

Based on the principle of operating an oil-filled-cable operation and the explanation of the oil-filling process provided in the cable operation and maintenance manual of submarine cables, this study investigated oil-pressure variation caused by gas generated as a result of cable faults. First, a set of oil-filled cables and their terminal oil-filled simulation system were designed in the laboratory, and a typical oil-filled-cable fault model was established according to the common faults of oil-filled cables observed in practice. Thereafter, ultrasonic signals of partial discharge (PD) under different fault models were obtained via validation experiments, which were performed by using oil-filled-cable simulation equipment. Subsequently, the ultrasonic signal mechanism was analyzed; these signals were generated via electric, thermal, and acoustic expansion and contraction, along with electric, mechanical, and acoustic electrostriction. Finally, upon processing the 400 experimental data groups, four practical parameters—maximum amplitude of the ultrasonic signal spectrum, D max , maximum frequency of the ultrasonic signals, f max , average ultrasonic signal energy, D av , and the ultrasonic signal amplitude coefficient, M—were designed to characterize the ultrasonic signals. These parameters can be used for subsequent pattern recognition. Thus, in this study, the terminal PD of an oil-filled marine cable was monitored.

Suggested Citation

  • Yulong Wang & Xiaohong Zhang & Lili Li & Jinyang Du & Junguo Gao, 2019. "Design of Partial Discharge Test Environment for Oil-Filled Submarine Cable Terminals and Ultrasonic Monitoring," Energies, MDPI, vol. 12(24), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4774-:d:298024
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/24/4774/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/24/4774/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stefan Tenbohlen & Sebastian Coenen & Mohammad Djamali & Andreas Müller & Mohammad Hamed Samimi & Martin Siegel, 2016. "Diagnostic Measurements for Power Transformers," Energies, MDPI, vol. 9(5), pages 1-25, May.
    2. Arthur F. Andrade & Edson G. Costa & Filipe L.M. Andrade & Clarice S.H. Soares & George R.S. Lira, 2019. "Design of Cable Termination for AC Breakdown Voltage Tests," Energies, MDPI, vol. 12(16), pages 1-14, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mehran Tahir & Stefan Tenbohlen, 2019. "A Comprehensive Analysis of Windings Electrical and Mechanical Faults Using a High-Frequency Model," Energies, MDPI, vol. 13(1), pages 1-25, December.
    2. Lefeng Cheng & Tao Yu & Guoping Wang & Bo Yang & Lv Zhou, 2018. "Hot Spot Temperature and Grey Target Theory-Based Dynamic Modelling for Reliability Assessment of Transformer Oil-Paper Insulation Systems: A Practical Case Study," Energies, MDPI, vol. 11(1), pages 1-26, January.
    3. Ioannis F. Gonos & Issouf Fofana, 2020. "Special Issue “Selected Papers from the 2018 IEEE International Conference on High Voltage Engineering (ICHVE 2018)”," Energies, MDPI, vol. 13(18), pages 1-5, September.
    4. Jiefeng Liu & Hanbo Zheng & Yiyi Zhang & Hua Wei & Ruijin Liao, 2017. "Grey Relational Analysis for Insulation Condition Assessment of Power Transformers Based Upon Conventional Dielectric Response Measurement," Energies, MDPI, vol. 10(10), pages 1-16, October.
    5. Szymon Banaszak & Konstanty Marek Gawrylczyk & Katarzyna Trela, 2020. "Frequency Response Modelling of Transformer Windings Connected in Parallel," Energies, MDPI, vol. 13(6), pages 1-13, March.
    6. Szymon Banaszak & Wojciech Szoka, 2018. "Cross Test Comparison in Transformer Windings Frequency Response Analysis," Energies, MDPI, vol. 11(6), pages 1-12, May.
    7. Fatih Atalar & Aysel Ersoy & Pawel Rozga, 2022. "Investigation of Effects of Different High Voltage Types on Dielectric Strength of Insulating Liquids," Energies, MDPI, vol. 15(21), pages 1-25, October.
    8. Patryk Bohatyrewicz & Szymon Banaszak, 2022. "Assessment Criteria of Changes in Health Index Values over Time—A Transformer Population Study," Energies, MDPI, vol. 15(16), pages 1-15, August.
    9. Alexandra I. Khalyasmaa & Pavel V. Matrenin & Stanislav A. Eroshenko & Vadim Z. Manusov & Andrey M. Bramm & Alexey M. Romanov, 2022. "Data Mining Applied to Decision Support Systems for Power Transformers’ Health Diagnostics," Mathematics, MDPI, vol. 10(14), pages 1-25, July.
    10. Mehran Tahir & Stefan Tenbohlen, 2021. "Transformer Winding Condition Assessment Using Feedforward Artificial Neural Network and Frequency Response Measurements," Energies, MDPI, vol. 14(11), pages 1-25, May.
    11. Satoru Miyazaki, 2021. "Detection of Winding Axial Displacement of a Real Transformer by Frequency Response Analysis without Fingerprint Data," Energies, MDPI, vol. 15(1), pages 1-14, December.
    12. Chunguang Suo & Yanan Ren & Wenbin Zhang & Yincheng Li & Yanyun Wang & Yi Ke, 2021. "Evaluation Method for Winding Performance of Distribution Transformer," Energies, MDPI, vol. 14(18), pages 1-25, September.
    13. Maciej Kuniewski, 2020. "FRA Diagnostics Measurement of Winding Deformation in Model Single-Phase Transformers Made with Silicon-Steel, Amorphous and Nanocrystalline Magnetic Cores," Energies, MDPI, vol. 13(10), pages 1-23, May.
    14. Ruohan Gong & Jiangjun Ruan & Jingzhou Chen & Yu Quan & Jian Wang & Cihan Duan, 2017. "Analysis and Experiment of Hot-Spot Temperature Rise of 110 kV Three-Phase Three-Limb Transformer," Energies, MDPI, vol. 10(8), pages 1-12, July.
    15. Feng Yang & Lin Du & Lijun Yang & Chao Wei & Youyuan Wang & Liman Ran & Peng He, 2018. "A Parameterization Approach for the Dielectric Response Model of Oil Paper Insulation Using FDS Measurements," Energies, MDPI, vol. 11(3), pages 1-17, March.
    16. Álvaro Jaramillo-Duque & Nicolás Muñoz-Galeano & José R. Ortiz-Castrillón & Jesús M. López-Lezama & Ricardo Albarracín-Sánchez, 2018. "Power Loss Minimization for Transformers Connected in Parallel with Taps Based on Power Chargeability Balance," Energies, MDPI, vol. 11(2), pages 1-12, February.
    17. Sergio Bustamante & Mario Manana & Alberto Arroyo & Raquel Martinez & Alberto Laso, 2020. "A Methodology for the Calculation of Typical Gas Concentration Values and Sampling Intervals in the Power Transformers of a Distribution System Operator," Energies, MDPI, vol. 13(22), pages 1-18, November.
    18. Alexander S. Karandaev & Igor M. Yachikov & Andrey A. Radionov & Ivan V. Liubimov & Nikolay N. Druzhinin & Ekaterina A. Khramshina, 2022. "Fuzzy Algorithms for Diagnosis of Furnace Transformer Insulation Condition," Energies, MDPI, vol. 15(10), pages 1-21, May.
    19. Christian Gianoglio & Edoardo Ragusa & Paolo Gastaldo & Federico Gallesi & Francesco Guastavino, 2021. "Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks," Energies, MDPI, vol. 14(15), pages 1-23, August.
    20. Ju Tang & Miao Jin & Fuping Zeng & Siyuan Zhou & Xiaoxing Zhang & Yi Yang & Yan Ma, 2017. "Feature Selection for Partial Discharge Severity Assessment in Gas-Insulated Switchgear Based on Minimum Redundancy and Maximum Relevance," Energies, MDPI, vol. 10(10), pages 1-14, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4774-:d:298024. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.