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Grey Relational Analysis for Insulation Condition Assessment of Power Transformers Based Upon Conventional Dielectric Response Measurement

Author

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  • Jiefeng Liu

    () (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
    Shijiazhuang Power Supply Branch of State Grid Electric Power Company, Shijiazhuang 050093, China
    These authors contributed equally to this work.)

  • Hanbo Zheng

    () (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
    State Grid Henan Electric Power Research Institute, Zhengzhou 450052, China
    These authors contributed equally to this work.)

  • Yiyi Zhang

    () (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
    These authors contributed equally to this work.)

  • Hua Wei

    () (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China)

  • Ruijin Liao

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

Abstract

Conventional dielectric response measurement techniques, for instance, recovery voltage measurement (RVM), frequency domain spectroscopy (FDS) and polarization–depolarization current (PDC) are effective nondestructive insulation monitoring techniques for oil-impregnated power transformers. Previous studies have focused mainly on some single type of dielectric measurement method. However, the condition of oil paper insulation in transformer is affected by many factors, so it is difficult to predict the insulation status by means of a single method. In this paper, the insulation condition assessment is performed by grey relational analysis (GRA) technique after carefully investigating different dielectric response measurement data. The insulation condition sensitive parameters of samples with unknown insulation status are extracted from different dielectric response measurement data and then these are used to contrast with the standard insulation state vector models established in controlled laboratory conditions by using GRA technique for predicting insulation condition. The performance of the proposed approach is tested using both the laboratory samples and a power transformer to demonstrate that it can provide reliable and effective insulation diagnosis.

Suggested Citation

  • 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, Open Access Journal, vol. 10(10), pages 1-16, October.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1526-:d:114290
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    References listed on IDEAS

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    1. repec:gam:jeners:v:10:y:2017:i:11:p:1842-:d:118444 is not listed on IDEAS
    2. repec:gam:jeners:v:11:y:2018:i:1:p:146-:d:125839 is not listed on IDEAS
    3. repec:gam:jeners:v:11:y:2018:i:4:p:913-:d:140839 is not listed on IDEAS
    4. repec:gam:jeners:v:11:y:2018:i:6:p:1448-:d:150587 is not listed on IDEAS

    More about this item

    Keywords

    recovery voltage measurement; polarization–depolarization current; frequency domain spectroscopy; grey relational analysis; oil-impregnated transformers;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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