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High-Voltage Cable Condition Assessment Method Based on Multi-Source Data Analysis

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  • Xiao-Kai Meng

    (College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 032100, China
    State Grid Shanxi Electric Power Research Institute, Taiyuan 032100, China)

  • Yan-Bing Jia

    (College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 032100, China)

  • Zhi-Heng Liu

    (College of Electronic Information Engineering, Hebei University, Baoding 071002, China
    Key Laboratory of the Ministry of Education on Optoelectronic Information Technology, Tianjin University, Tianjin 300072, China)

  • Zhi-Qiang Yu

    (College of Electronic Information Engineering, Hebei University, Baoding 071002, China)

  • Pei-Jie Han

    (State Grid Shanxi Electric Power Corporation, Taiyuan 032100, China)

  • Zhu-Mao Lu

    (State Grid Shanxi Electric Power Research Institute, Taiyuan 032100, China)

  • Tao Jin

    (State Grid Shanxi Electric Power Research Institute, Taiyuan 032100, China)

Abstract

In view of the problem that the weight value given by the previous state evaluation method is fixed and single and cannot analyze the influence of the weight vector deviation on the evaluation result, a method based on the weight space Markov chain and Monte Carlo method (Markov chains Monte Carlo, MCMC) is proposed. The sampling method is used for evaluating the condition of high-voltage cables. The weight vector set obtained by MCMC sampling and the comprehensive degradation degree of the high-voltage cable sample are weighted and summed then compared in pairs to obtain the comprehensive degradation degree result. The status probability value and overall priority ranking probability of the object to be evaluated are obtained based on probability statistics, and the order of maintenance is determined according to the status probability value and the ranking result. It is realized that the cable line that needs to be identified in the follow-up defect is clarified according to the evaluation result. This is helpful for operational and maintenance personnel to more accurately implement the maintenance plan for the cable and improve the operational and maintenance efficiency.

Suggested Citation

  • Xiao-Kai Meng & Yan-Bing Jia & Zhi-Heng Liu & Zhi-Qiang Yu & Pei-Jie Han & Zhu-Mao Lu & Tao Jin, 2022. "High-Voltage Cable Condition Assessment Method Based on Multi-Source Data Analysis," Energies, MDPI, vol. 15(4), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1369-:d:749137
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    References listed on IDEAS

    as
    1. Christopher Nemeth & Paul Fearnhead, 2021. "Stochastic Gradient Markov Chain Monte Carlo," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(533), pages 433-450, January.
    2. Pengyu Wang & Gang Liu & Hui Ma & Yigang Liu & Tao Xu, 2017. "Investigation of the Ampacity of a Prefabricated Straight-Through Joint of High Voltage Cable," Energies, MDPI, vol. 10(12), pages 1-17, December.
    3. Christoph Jörgens & Markus Clemens, 2020. "A Review about the Modeling and Simulation of Electro-Quasistatic Fields in HVDC Cable Systems," Energies, MDPI, vol. 13(19), pages 1-42, October.
    4. Mansoor Asif & Ho-Yun Lee & Kyu-Hoon Park & Bang-Wook Lee, 2019. "Accurate Evaluation of Steady-State Sheath Voltage and Current in HVDC Cable Using Electromagnetic Transient Simulation," Energies, MDPI, vol. 12(21), pages 1-17, October.
    5. Yvonne Späck-Leigsnering & Greta Ruppert & Erion Gjonaj & Herbert De Gersem & Myriam Koch, 2021. "Towards Electrothermal Optimization of a HVDC Cable Joint Based on Field Simulation," Energies, MDPI, vol. 14(10), pages 1-13, May.
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