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

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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, 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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    Cited by:

    1. Hanbo Zheng & Jiefeng Liu & Yiyi Zhang & Yijie Ma & Yang Shen & Xiaochen Zhen & Zilai Chen, 2018. "Effectiveness Analysis and Temperature Effect Mechanism on Chemical and Electrical-Based Transformer Insulation Diagnostic Parameters Obtained from PDC Data," Energies, MDPI, vol. 11(1), pages 1-17, January.
    2. Lefeng Cheng & Tao Yu, 2018. "Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey," Energies, MDPI, vol. 11(4), pages 1-69, April.
    3. Sameh Ziad Ahmed Dabbak & Hazlee Azil Illias & Bee Chin Ang & Nurul Ain Abdul Latiff & Mohamad Zul Hilmey Makmud, 2018. "Electrical Properties of Polyethylene/Polypropylene Compounds for High-Voltage Insulation," Energies, MDPI, vol. 11(6), pages 1-13, June.
    4. Zhilin Lyu & Qing Wei & Yiyi Zhang & Junhui Zhao & Emad Manla, 2018. "Adaptive Virtual Impedance Droop Control Based on Consensus Control of Reactive Current," Energies, MDPI, vol. 11(7), pages 1-17, July.
    5. Fenglan Tian & Zhongzhao Jing & Huan Zhao & Enze Zhang & Jiefeng Liu, 2019. "A Synthetic Condition Assessment Model for Power Transformers Using the Fuzzy Evidence Fusion Method," Energies, MDPI, vol. 12(5), pages 1-17, March.
    6. Yiyi Zhang & Jiefeng Liu & Hanbo Zheng & Hua Wei & Ruijin Liao, 2017. "Study on Quantitative Correlations between the Ageing Condition of Transformer Cellulose Insulation and the Large Time Constant Obtained from the Extended Debye Model," Energies, MDPI, vol. 10(11), pages 1-17, November.
    7. Minghui Ou & Hua Wei & Yiyi Zhang & Jiancheng Tan, 2019. "A Dynamic Adam Based Deep Neural Network for Fault Diagnosis of Oil-Immersed Power Transformers," Energies, MDPI, vol. 12(6), pages 1-16, March.
    8. Jiake Fang & Hanbo Zheng & Jiefeng Liu & Junhui Zhao & Yiyi Zhang & Ke Wang, 2018. "A Transformer Fault Diagnosis Model Using an Optimal Hybrid Dissolved Gas Analysis Features Subset with Improved Social Group Optimization-Support Vector Machine Classifier," Energies, MDPI, vol. 11(8), pages 1-18, July.

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