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A Novel Approach to Assess Power Transformer Winding Conditions Using Regression Analysis and Frequency Response Measurements

Author

Listed:
  • Bonginkosi A. Thango

    (Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg 2028, South Africa)

  • Agha F. Nnachi

    (Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0183, South Africa)

  • Goodness A. Dlamini

    (Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg 2028, South Africa)

  • Pitshou N. Bokoro

    (Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg 2028, South Africa)

Abstract

A frequency response analysis (FRA) is a well-known technique for evaluating the mechanical stability of a power transformer’s active part components. FRA’s measuring practices have been industrialised and are codified in IEEE and IEC standards. However, because there is no valid coding in the standard, the interpretation of FRA data is still far from being a widely acknowledged and authoritative approach. This study proposes an innovative fault segmentation and localisation technique based on FRA data. The algorithm is based on regression analysis to estimate the repeatability and relationship between the FRA fingerprint and the latest measured data. Initially, the measuring frequency is discretised into three regions to narrow the location of the fault; the regression model of the fingerprint and current FRA data are then evaluated. As a benchmark, two statistical indicators are the employed benchmark against the proposed method. Finally, the proposed scheme identifies and characterises various transformer conditions, such as healthy windings, axial and radial winding deformations, core deformation and electrical faults. The database used in this study consists of FRA measurements from 70 mineral-oil-immersed power transformers of different designs, ratings and manufacturers that were physically inspected for various faults and comparable frequency regions. The results achieved corroborate the efficacy of the proposed regression analysis fault recognition algorithm (RAFRA) model for transformer fault diagnosis using FRA. Further recommendations are made to address the reproducibility concerns induced by multiple FRA testing conditions.

Suggested Citation

  • Bonginkosi A. Thango & Agha F. Nnachi & Goodness A. Dlamini & Pitshou N. Bokoro, 2022. "A Novel Approach to Assess Power Transformer Winding Conditions Using Regression Analysis and Frequency Response Measurements," Energies, MDPI, vol. 15(7), pages 1-22, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2335-:d:777642
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    References listed on IDEAS

    as
    1. Mehran Tahir & Stefan Tenbohlen, 2021. "Transformer Winding Condition Assessment Using Feedforward Artificial Neural Network and Frequency Response Measurements," Energies, MDPI, vol. 14(11), pages 1-25, May.
    2. Sifeddine Abdi & Noureddine Harid & Leila Safiddine & Ahmed Boubakeur & Abderrahmane (Manu) Haddad, 2021. "The Correlation of Transformer Oil Electrical Properties with Water Content Using a Regression Approach," Energies, MDPI, vol. 14(8), pages 1-14, April.
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    Citations

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    Cited by:

    1. Bonginkosi A. Thango, 2022. "Application of the Analysis of Variance (ANOVA) in the Interpretation of Power Transformer Faults," Energies, MDPI, vol. 15(19), pages 1-17, October.
    2. 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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