IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i7p2335-d777642.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/7/2335/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/7/2335/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yunhe Luo & Xiaosong Zou & Wei Xiong & Xufeng Yuan & Kui Xu & Yu Xin & Ruoyu Zhang, 2023. "Dynamic State Evaluation Method of Power Transformer Based on Mahalanobis–Taguchi System and Health Index," Energies, MDPI, vol. 16(6), pages 1-16, March.
    2. Wenqi Ge & Chenchen Zhang & Yi Xie & Ming Yu & Youhua Wang, 2021. "Analysis of the Electromechanical Characteristics of Power Transformer under Different Residual Fluxes," Energies, MDPI, vol. 14(24), pages 1-22, December.
    3. 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.
    4. Mehran Tahir & Stefan Tenbohlen, 2023. "Transformer Winding Fault Classification and Condition Assessment Based on Random Forest Using FRA," Energies, MDPI, vol. 16(9), pages 1-16, April.
    5. Sifeddine Abdi & Abderrahmane Manu Haddad & Noureddine Harid & Ahmed Boubakeur, 2022. "Modelling the Effect of Thermal Aging on Transformer Oil Electrical Characteristics Using a Regression Approach," Energies, MDPI, vol. 16(1), pages 1-12, December.
    6. Regelii Suassuna de Andrade Ferreira & Patrick Picher & Hassan Ezzaidi & Issouf Fofana, 2021. "A Machine-Learning Approach to Identify the Influence of Temperature on FRA Measurements," Energies, MDPI, vol. 14(18), pages 1-14, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2335-:d:777642. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.