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Identification of Power Transformer Winding Fault Types by a Hierarchical Dimension Reduction Classifier

Author

Listed:
  • Ziwei Zhang

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
    Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610213, China)

  • Wensheng Gao

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
    Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610213, China)

  • Tusongjiang Kari

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Huan Lin

    (Swatow Power Supply Bureau, Guangdong Power Grid Co., Ltd., Swatow 515000, China)

Abstract

Frequency response analysis (FRA) demonstrates significant advantages in the diagnosis of transformer winding faults. The instrument market desires intelligent diagnostic functions to ensure that the FRA technique is more practically useful. In this paper, a hierarchical dimension reduction (HDR) classifier is proposed to identify types of typical incipient winding faults. The classifier procedure is hierarchical. First, measured frequency response (FR) curves are preprocessed using binarization and binary erosion to normalize FR data. Second, the pre-processed data are divided into groups according to the definition of dynamic frequency sub-bands. Then, hybrid algorithms comprised of two conventional and two novel quantitative indices are used to reduce the dimension of the FR data and extract the features for identifying typical types of transformer winding faults. The classifier provides an integration of a priori expertise and quantitative analysis in the furtherance of the automatic identification of FR data. Twenty-six sets of FR data from different types of power transformers with multiple types of winding faults were collected from an experimental simulation, literature, and real tests performed by a grid company. Finally, real case studies were conducted to verify the performance of the HDR classifier in the automatic identification of transformer winding faults.

Suggested Citation

  • Ziwei Zhang & Wensheng Gao & Tusongjiang Kari & Huan Lin, 2018. "Identification of Power Transformer Winding Fault Types by a Hierarchical Dimension Reduction Classifier," Energies, MDPI, vol. 11(9), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2434-:d:169736
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    References listed on IDEAS

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    1. Szymon Banaszak & Wojciech Szoka, 2018. "Cross Test Comparison in Transformer Windings Frequency Response Analysis," Energies, MDPI, vol. 11(6), pages 1-12, May.
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    Cited by:

    1. Marek Florkowski & Jakub Furgał & Maciej Kuniewski, 2020. "Propagation of Overvoltages in the Form of Impulse, Chopped and Oscillating Waveforms in Transformer Windings—Time and Frequency Domain Approach," Energies, MDPI, vol. 13(2), pages 1-16, January.
    2. Chunguang Suo & Yanan Ren & Wenbin Zhang & Yincheng Li & Yanyun Wang & Yi Ke, 2021. "Evaluation Method for Winding Performance of Distribution Transformer," Energies, MDPI, vol. 14(18), pages 1-25, September.
    3. Maciej Kuniewski, 2020. "FRA Diagnostics Measurement of Winding Deformation in Model Single-Phase Transformers Made with Silicon-Steel, Amorphous and Nanocrystalline Magnetic Cores," Energies, MDPI, vol. 13(10), pages 1-23, May.
    4. Song Wang & Shuang Wang & Ying Cui & Jie Long & Fuqiang Ren & Shengchang Ji & Shuhong Wang, 2020. "An Experimental Study of the Sweep Frequency Impedance Method on the Winding Deformation of an Onsite Power Transformer," Energies, MDPI, vol. 13(14), pages 1-13, July.

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