IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i5p1209-d1603301.html
   My bibliography  Save this article

A Comprehensive Review of Transformer Winding Diagnostics: Integrating Frequency Response Analysis with Machine Learning Approaches

Author

Listed:
  • Meysam Beheshti Asl

    (Aging of Oil-Filled Equipment on High Voltage Lines (ViAHT), Department of Applied Sciences (DSA), University of Quebec at Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada)

  • Issouf Fofana

    (Aging of Oil-Filled Equipment on High Voltage Lines (ViAHT), Department of Applied Sciences (DSA), University of Quebec at Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada)

  • Fethi Meghnefi

    (Aging of Oil-Filled Equipment on High Voltage Lines (ViAHT), Department of Applied Sciences (DSA), University of Quebec at Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada)

  • Youssouf Brahami

    (Aging of Oil-Filled Equipment on High Voltage Lines (ViAHT), Department of Applied Sciences (DSA), University of Quebec at Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada)

  • Joao Pedro Da Costa Souza

    (Aging of Oil-Filled Equipment on High Voltage Lines (ViAHT), Department of Applied Sciences (DSA), University of Quebec at Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada)

Abstract

Frequency Response Analysis (FRA) is a proven method for detecting mechanical faults in transformers, such as winding deformations and short circuits. However, traditional FRA interpretation relies heavily on visual and subjective comparison of frequency response curves, which can introduce human bias and lead to inconsistent results. Integrating Machine Learning (ML) with FRA can significantly enhance fault diagnosis by automatically identifying complex patterns within the data that are difficult to detect using through human analysis. This integration can automate diagnostics, enhance accuracy, improve predictive maintenance, reduce reliance on expert interpretation and curtail operational costs. This paper reviews the application of FRA and ML alongside complementary techniques for transformer winding health assessment.

Suggested Citation

  • Meysam Beheshti Asl & Issouf Fofana & Fethi Meghnefi & Youssouf Brahami & Joao Pedro Da Costa Souza, 2025. "A Comprehensive Review of Transformer Winding Diagnostics: Integrating Frequency Response Analysis with Machine Learning Approaches," Energies, MDPI, vol. 18(5), pages 1-38, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1209-:d:1603301
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/5/1209/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/5/1209/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Meysam Beheshti Asl & Issouf Fofana & Fethi Meghnefi, 2024. "Review of Various Sensor Technologies in Monitoring the Condition of Power Transformers," Energies, MDPI, vol. 17(14), pages 1-40, July.
    2. Avinash Srikanta Murthy & Norhafiz Azis & Salem Al-Ameri & Mohd Fairouz Mohd Yousof & Jasronita Jasni & Mohd Aizam Talib, 2018. "Investigation of the Effect of Winding Clamping Structure on Frequency Response Signature of 11 kV Distribution Transformer," Energies, MDPI, vol. 11(9), pages 1-13, September.
    3. Mohammed Alenezi & Fatih Anayi & Michael Packianather & Mokhtar Shouran, 2024. "Enhancing Transformer Protection: A Machine Learning Framework for Early Fault Detection," Sustainability, MDPI, vol. 16(23), pages 1-23, December.
    4. ZhenHua Li & Yujie Zhang & Ahmed Abu-Siada & Xingxin Chen & Zhenxing Li & Yanchun Xu & Lei Zhang & Yue Tong, 2021. "Fault Diagnosis of Transformer Windings Based on Decision Tree and Fully Connected Neural Network," Energies, MDPI, vol. 14(6), pages 1-14, March.
    5. 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.
    6. Szymon Banaszak & Wojciech Szoka, 2018. "Cross Test Comparison in Transformer Windings Frequency Response Analysis," Energies, MDPI, vol. 11(6), pages 1-12, May.
    7. 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.
    8. Vasiliki Rokani & Stavros D. Kaminaris & Petros Karaisas & Dimitrios Kaminaris, 2023. "Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques," Mathematics, MDPI, vol. 11(22), pages 1-33, November.
    9. Micah Phillip & Arvind Singh & Craig J. Ramlal, 2023. "Narrow Band Frequency Response Analysis of Power Transformers with Deep Learning," Energies, MDPI, vol. 16(17), pages 1-14, September.
    10. 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.
    11. Katarzyna Trela & Konstanty Marek Gawrylczyk, 2024. "Modeling of Axial Displacements of Transformer Windings for Frequency Response Analysis Diagnosis," Energies, MDPI, vol. 17(13), pages 1-16, July.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Szymon Banaszak & Konstanty Marek Gawrylczyk & Katarzyna Trela, 2020. "Frequency Response Modelling of Transformer Windings Connected in Parallel," Energies, MDPI, vol. 13(6), pages 1-13, March.
    6. Xiaoxia Liang & Ming Zhang & Guojin Feng & Duo Wang & Yuchun Xu & Fengshou Gu, 2023. "Few-Shot Learning Approaches for Fault Diagnosis Using Vibration Data: A Comprehensive Review," Sustainability, MDPI, vol. 15(20), pages 1-17, October.
    7. Avinash Srikanta Murthy & Norhafiz Azis & Jasronita Jasni & Mohammad Lutfi Othman & Mohd Fairouz Mohd Yousof & Mohd Aizam Talib, 2020. "Extraction of winding parameters for 33/11 kV, 30 MVA transformer based on finite element method for frequency response modelling," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-22, August.
    8. Zhannan Guo & Yinlin Hao & Hanwen Shi & Zhenyu Wu & Yuhu Wu & Ximing Sun, 2023. "A Fault Diagnosis Algorithm for the Dedicated Equipment Based on the CNN-LSTM Mechanism," Energies, MDPI, vol. 16(13), pages 1-16, July.
    9. 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.
    10. 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.
    11. Lefa Zhao & Yafei Zhu & Tianyu Zhao, 2022. "Deep Learning-Based Remaining Useful Life Prediction Method with Transformer Module and Random Forest," Mathematics, MDPI, vol. 10(16), pages 1-15, August.
    12. Bustamante, Sergio & Manana, Mario & Arroyo, Alberto & Laso, Alberto & Martinez, Raquel, 2024. "Evolution of graphical methods for the identification of insulation faults in oil-immersed power transformers: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    13. Jonathan Velasco Costa & Diogo F. F. da Silva & Paulo J. Costa Branco, 2022. "Large-Power Transformers: Time Now for Addressing Their Monitoring and Failure Investigation Techniques," Energies, MDPI, vol. 15(13), pages 1-59, June.
    14. Xuemin Huang & Xiaoliang Zhuang & Fangyuan Tian & Zheng Niu & Yujie Chen & Qian Zhou & Chao Yuan, 2025. "A Hybrid ARIMA-LSTM-XGBoost Model with Linear Regression Stacking for Transformer Oil Temperature Prediction," Energies, MDPI, vol. 18(6), pages 1-22, March.
    15. Tomasz Piotrowski & Pawel Rozga & Ryszard Kozak, 2019. "Comparative Analysis of the Results of Diagnostic Measurements with an Internal Inspection of Oil-Filled Power Transformers," Energies, MDPI, vol. 12(11), pages 1-18, June.
    16. Zbigniew Nadolny, 2023. "Design and Optimization of Power Transformer Diagnostics," Energies, MDPI, vol. 16(18), pages 1-7, September.
    17. Sara Mantach & Abdulla Lutfi & Hamed Moradi Tavasani & Ahmed Ashraf & Ayman El-Hag & Behzad Kordi, 2022. "Deep Learning in High Voltage Engineering: A Literature Review," Energies, MDPI, vol. 15(14), pages 1-32, July.
    18. 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.
    19. Min-Soo Kim & Do-Hyun Kim & Dong-Keun Jeong & Jang-Mok Kim & Hee-Je Kim, 2020. "Soft Start-Up Control Strategy for Dual Active Bridge Converter with a Supercapacitor," Energies, MDPI, vol. 13(16), pages 1-19, August.
    20. Zhen Huang & Xuechun Xiao & Yuan Gao & Yonghong Xia & Tomislav Dragičević & Pat Wheeler, 2023. "Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications," Energies, MDPI, vol. 16(17), pages 1-26, August.

    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:18:y:2025:i:5:p:1209-:d:1603301. 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.