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A Two-Dimensional Cloud Model for Condition Assessment of HVDC Converter Transformers

Author

Listed:
  • Jian Li

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400030, China)

  • Zhiman He

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400030, China)

  • Youyuan Wang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400030, China)

  • Jinzhuang Lv

    (HVDC Technology Research Department, Electric Power Research Institute, China Southern Power Grid, Guangzhou 510623, China)

  • Linjie Zhao

    (HVDC Technology Research Department, Electric Power Research Institute, China Southern Power Grid, Guangzhou 510623, China)

Abstract

Converter transformers are the key and the most important components in high voltage direct current (HVDC) power transmission systems. Statistics show that the failure rate of HVDC converter transformers is approximately twice of that of transformers in AC power systems. This paper presents an approach integrated with a two-dimensional cloud model and an entropy-based weight model to evaluate the condition of HVDC converter transformers. The integrated approach can describe the complexity of HVDC converter transformers and achieve an effective assessment of their condition. Data from electrical testing, DGA, oil testing, and visual inspection were chosen to form the double-level assessment index system. Analysis results show that the integrated approach is capable of providing a relevant and effective assessment which in turn, provides valuable information for the maintenance of HVDC converter transformers.

Suggested Citation

  • Jian Li & Zhiman He & Youyuan Wang & Jinzhuang Lv & Linjie Zhao, 2012. "A Two-Dimensional Cloud Model for Condition Assessment of HVDC Converter Transformers," Energies, MDPI, vol. 5(1), pages 1-11, January.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:1:p:157-167:d:15806
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    References listed on IDEAS

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    1. Thomas Tamo Tatietse & Joseph Voufo, 2009. "Fault Diagnosis on Medium Voltage (MV) Electric Power Distribution Networks: The Case of the Downstream Network of the AES-SONEL Ngousso Sub-Station," Energies, MDPI, vol. 2(2), pages 1-15, April.
    2. Tianyan Jiang & Jian Li & Yuanbing Zheng & Caixin Sun, 2011. "Improved Bagging Algorithm for Pattern Recognition in UHF Signals of Partial Discharges," Energies, MDPI, vol. 4(7), pages 1-15, July.
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    Cited by:

    1. Jiaxin Lu & Weijun Wang & Yingchao Zhang & Song Cheng, 2017. "Multi-Objective Optimal Design of Stand-Alone Hybrid Energy System Using Entropy Weight Method Based on HOMER," Energies, MDPI, vol. 10(10), pages 1-17, October.
    2. Luis Hernández-Callejo, 2019. "A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids," Energies, MDPI, vol. 12(9), pages 1-50, April.

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