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A Thermal Probability Density–Based Method to Detect the Internal Defects of Power Cable Joints

Author

Listed:
  • Li Zhang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Xiyue LuoYang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Yanjie Le

    (Zhoushan Power Company of State Grid, Zhoushan 316021, China)

  • Fan Yang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Chun Gan

    (Zhoushan Power Company of State Grid, Zhoushan 316021, China)

  • Yinxian Zhang

    (Zhoushan Power Company of State Grid, Zhoushan 316021, China)

Abstract

Internal defects inside power cable joints due to unqualified construction is the main issue of power cable failures, hence in this paper a method based on thermal probability density function to detect the internal defects of power cable joints is presented. First, the model to calculate the thermal distribution of power cable joints is set up and the thermal distribution is calculated. Then a thermal probability density (TPD)-based method that gives the statistics of isothermal points is presented. The TPD characteristics of normal power cable joints and those with internal defects, including insulation eccentricity and unqualified connection of conductors, are analyzed. The results indicate that TPD differs with the internal state of cable joints. Finally, experiments were conducted in which surface thermal distribution was measured by FLIR SC7000, and the corresponding TPDs are discussed.

Suggested Citation

  • Li Zhang & Xiyue LuoYang & Yanjie Le & Fan Yang & Chun Gan & Yinxian Zhang, 2018. "A Thermal Probability Density–Based Method to Detect the Internal Defects of Power Cable Joints," Energies, MDPI, vol. 11(7), pages 1-13, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1674-:d:154768
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    Citations

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    Cited by:

    1. Virginia Negri & Alessandro Mingotti & Roberto Tinarelli & Lorenzo Peretto, 2023. "Comparison of Algorithms for the AI-Based Fault Diagnostic of Cable Joints in MV Networks," Energies, MDPI, vol. 16(1), pages 1-20, January.
    2. Alessandro Mingotti & Federica Costa & Lorenzo Peretto & Roberto Tinarelli & Paolo Mazza, 2021. "Modeling Stray Capacitances of High-Voltage Capacitive Dividers for Conventional Measurement Setups," Energies, MDPI, vol. 14(5), pages 1-15, February.
    3. Jiahong He & Kang He & Longfei Cui, 2019. "Charge-Simulation-Based Electric Field Analysis and Electrical Tree Propagation Model with Defects in 10 kV XLPE Cable Joint," Energies, MDPI, vol. 12(23), pages 1-22, November.
    4. Tommaso Bragatto & Alberto Cerretti & Luigi D’Orazio & Fabio Massimo Gatta & Alberto Geri & Marco Maccioni, 2019. "Thermal Effects of Ground Faults on MV Joints and Cables," Energies, MDPI, vol. 12(18), pages 1-15, September.
    5. Carlo Olivieri & Francesco de Paulis & Antonio Orlandi & Giorgio Giannuzzi & Roberto Salvati & Roberto Zaottini & Carlo Morandini & Lorenzo Mocarelli, 2019. "Remote Monitoring of Joints Status on In-Service High-Voltage Overhead Lines," Energies, MDPI, vol. 12(6), pages 1-17, March.
    6. Francisco G. Montoya & Raúl Baños & Alfredo Alcayde & Francisco Manzano-Agugliaro, 2019. "Optimization Methods Applied to Power Systems," Energies, MDPI, vol. 12(12), pages 1-8, June.

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