IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i18p4846-d414530.html
   My bibliography  Save this article

Effects of Fast Elongation on Switching Arcs Characteristics in Fast Air Switches

Author

Listed:
  • Ali Kadivar

    (Department of Electric Power Engineering, Norwegian University of Science and Technology, 7034 Trondheim, Norway
    Department of Transmission Line and Substation Equipment, Niroo Research Institute (NRI), End of Dadman Street, Shahrak Ghods, Tehran 1468613113, Iran)

  • Kaveh Niayesh

    (Department of Electric Power Engineering, Norwegian University of Science and Technology, 7034 Trondheim, Norway)

Abstract

This paper is devoted to investigating the effects of high-speed elongation of arcs inside ultra-fast switches ( u contact ≈ 5–80 m/s), through a 2-D time-dependent model, in Cartesian coordinates. Two air arcs in series, one between a stationary anode and a moving cathode and the other between a stationary cathode and a moving anode in the arc chamber, are considered. A variable speed experimental setup through a Thomson drive actuator is designed to support this study. A computational fluid dynamics (CFD) equations system is solved for fluid velocity, pressure, temperature, and electric potential, as well as the magnetic vector potential. Electron emission mechanisms on the contact surface and induced current density due to magnetic field changes are also considered to describe the arc root formation, arc bending, lengthening, and calculating the arc current density, as well as the contact temperatures, in a better way. Data processing techniques are utilized to derive instantaneous core shape and profiles of the arc to investigate thermo-electrical characteristics during the elongation progress. The results are compared with another experimentally verified magnetohydrodynamics model of a fixed-length, free-burning arc in the air. The simulation and experimental results confirm each other.

Suggested Citation

  • Ali Kadivar & Kaveh Niayesh, 2020. "Effects of Fast Elongation on Switching Arcs Characteristics in Fast Air Switches," Energies, MDPI, vol. 13(18), pages 1-23, September.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4846-:d:414530
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/18/4846/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/18/4846/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liu, Kailong & Ashwin, T.R. & Hu, Xiaosong & Lucu, Mattin & Widanage, W. Dhammika, 2020. "An evaluation study of different modelling techniques for calendar ageing prediction of lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wen Wang & Zhibing Li & Keli Gao & Enyuan Dong & Xuebin Qu & Xiaodong Xu, 2022. "Dynamic Characteristics of Transverse-Magnetic-Field Induced Arc for Plasma-Jet-Triggered Protective Gas Switch in Hybrid UHVDC System," Energies, MDPI, vol. 15(16), pages 1-19, August.

    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. Rakshith Subramanya & Matti Yli-Ojanperä & Seppo Sierla & Taneli Hölttä & Jori Valtakari & Valeriy Vyatkin, 2021. "A Virtual Power Plant Solution for Aggregating Photovoltaic Systems and Other Distributed Energy Resources for Northern European Primary Frequency Reserves," Energies, MDPI, vol. 14(5), pages 1-23, February.
    2. Li, Alan G. & West, Alan C. & Preindl, Matthias, 2022. "Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review," Applied Energy, Elsevier, vol. 316(C).
    3. Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wei, Xuezhe & Shang, Wenlong & Dai, Haifeng, 2022. "A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 322(C).
    4. Jia Guo & Yaqi Li & Kjeld Pedersen & Daniel-Ioan Stroe, 2021. "Lithium-Ion Battery Operation, Degradation, and Aging Mechanism in Electric Vehicles: An Overview," Energies, MDPI, vol. 14(17), pages 1-22, August.
    5. Wang, Shunli & Takyi-Aninakwa, Paul & Jin, Siyu & Yu, Chunmei & Fernandez, Carlos & Stroe, Daniel-Ioan, 2022. "An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation," Energy, Elsevier, vol. 254(PA).
    6. Lluís Trilla & Lluc Canals Casals & Jordi Jacas & Pol Paradell, 2022. "Dual Extended Kalman Filter for State of Charge Estimation of Lithium–Sulfur Batteries," Energies, MDPI, vol. 15(19), pages 1-14, September.
    7. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    8. Guo, Yuanjun & Yang, Zhile & Liu, Kailong & Zhang, Yanhui & Feng, Wei, 2021. "A compact and optimized neural network approach for battery state-of-charge estimation of energy storage system," Energy, Elsevier, vol. 219(C).
    9. Neha Bhushan & Saad Mekhilef & Kok Soon Tey & Mohamed Shaaban & Mehdi Seyedmahmoudian & Alex Stojcevski, 2022. "Overview of Model- and Non-Model-Based Online Battery Management Systems for Electric Vehicle Applications: A Comprehensive Review of Experimental and Simulation Studies," Sustainability, MDPI, vol. 14(23), pages 1-31, November.
    10. Tang, Xiaopeng & Liu, Kailong & Lu, Jingyi & Liu, Boyang & Wang, Xin & Gao, Furong, 2020. "Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter," Applied Energy, Elsevier, vol. 280(C).
    11. Li, Penghua & Zhang, Zijian & Grosu, Radu & Deng, Zhongwei & Hou, Jie & Rong, Yujun & Wu, Rui, 2022. "An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    12. Wang, Shunli & Fan, Yongcun & Jin, Siyu & Takyi-Aninakwa, Paul & Fernandez, Carlos, 2023. "Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    13. Shunli Wang & Pu Ren & Paul Takyi-Aninakwa & Siyu Jin & Carlos Fernandez, 2022. "A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 15(14), pages 1-27, July.
    14. Chunxiang Zhu & Zhiwei He & Zhengyi Bao & Changcheng Sun & Mingyu Gao, 2023. "Prognosis of Lithium-Ion Batteries’ Remaining Useful Life Based on a Sequence-to-Sequence Model with Variational Mode Decomposition," Energies, MDPI, vol. 16(2), pages 1-16, January.
    15. Suqi Zhang & Ningjing Zhang & Ziqi Zhang & Ying Chen, 2022. "Electric Power Load Forecasting Method Based on a Support Vector Machine Optimized by the Improved Seagull Optimization Algorithm," Energies, MDPI, vol. 15(23), pages 1-17, December.

    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:13:y:2020:i:18:p:4846-:d:414530. 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.