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Detection of Winding Axial Displacement of a Real Transformer by Frequency Response Analysis without Fingerprint Data

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  • Satoru Miyazaki

    (Central Research Institute of Electric Power Industry, 2-6-1, Nagasaka, Yokosuka 240-0196, Kanagawa, Japan)

Abstract

Detection of the axial displacement of power-transformer winding is important to ensure its highly reliable operation. Frequency response analysis is a promising candidate in detecting the axial displacement. However, a method of detecting the axial displacement at an incipient stage without the need for fingerprint data has not been investigated yet. This paper focuses on resonances showing a bipolar signature in the transfer function of inductive interwinding measurement, which is sensitive to the axial displacement of the winding. Transfer functions in the inductive interwinding measurements of eight power transformers are measured before shipping to elucidate the features of resonances showing a bipolar signature. The measured resonances showing the bipolar signature can be divided into the “stair type” and the “crossing-curve type”. It is found that the grounding points in an inductive interwinding measurement determine the type of resonance showing the bipolar signature, irrespective of the type of winding, such as interleaved or multilayer winding, the winding arrangement, and the existence of stabilizing and tertiary windings. On the basis of this finding, a method of detecting the axial displacement of a transformer winding is proposed. In the proposed method, the amplitudes of the resonances among three phases are compared, or the three-phase pattern of the resonances is compared with normal patterns. Therefore, the proposed method is applicable to three-phase transformers without fingerprint data. The proposed method is applied to a real transformer that experienced a ground fault due to a lightning strike at a nearby transmission tower, and the effectiveness of the proposed method is confirmed.

Suggested Citation

  • Satoru Miyazaki, 2021. "Detection of Winding Axial Displacement of a Real Transformer by Frequency Response Analysis without Fingerprint Data," Energies, MDPI, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:15:y:2021:i:1:p:200-:d:713384
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    References listed on IDEAS

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    1. Stefan Tenbohlen & Sebastian Coenen & Mohammad Djamali & Andreas Müller & Mohammad Hamed Samimi & Martin Siegel, 2016. "Diagnostic Measurements for Power Transformers," Energies, MDPI, vol. 9(5), pages 1-25, May.
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    Cited by:

    1. Omid Elahi & Reza Behkam & Gevork B. Gharehpetian & Fazel Mohammadi, 2022. "Diagnosing Disk-Space Variation in Distribution Power Transformer Windings Using Group Method of Data Handling Artificial Neural Networks," Energies, MDPI, vol. 15(23), pages 1-32, November.

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