IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i11p3227-d566627.html
   My bibliography  Save this article

Transformer Winding Condition Assessment Using Feedforward Artificial Neural Network and Frequency Response Measurements

Author

Listed:
  • Mehran Tahir

    (Institute of Power Transmission and High Voltage Technology (IEH), Stuttgart University, Pfaffenwaldring 47, 70569 Stuttgart, Germany)

  • Stefan Tenbohlen

    (Institute of Power Transmission and High Voltage Technology (IEH), Stuttgart University, Pfaffenwaldring 47, 70569 Stuttgart, Germany)

Abstract

Frequency response analysis (FRA) is a well-known method to assess the mechanical integrity of the active parts of the power transformer. The measurement procedures of FRA are standardized as described in the IEEE and IEC standards. However, the interpretation of FRA results is far from reaching an accepted and definitive methodology as there is no reliable code available in the standard. As a contribution to this necessity, this paper presents an intelligent fault detection and classification algorithm using FRA results. The algorithm is based on a multilayer, feedforward, backpropagation artificial neural network (ANN). First, the adaptive frequency division algorithm is developed and various numerical indicators are used to quantify the differences between FRA traces and obtain feature sets for ANN. Finally, the classification model of ANN is developed to detect and classify different transformer conditions, i.e., healthy windings, healthy windings with saturated core, mechanical deformations, electrical faults, and reproducibility issues due to different test conditions. The database used in this study consists of FRA measurements from 80 power transformers of different designs, ratings, and different manufacturers. The results obtained give evidence of the effectiveness of the proposed classification model for power transformer fault diagnosis using FRA.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3227-:d:566627
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/11/3227/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/11/3227/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    2. Mehran Tahir & Stefan Tenbohlen, 2019. "A Comprehensive Analysis of Windings Electrical and Mechanical Faults Using a High-Frequency Model," Energies, MDPI, vol. 13(1), pages 1-25, December.
    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. 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. 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.
    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. 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.
    5. 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.
    6. 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.

    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. Yulong Wang & Xiaohong Zhang & Lili Li & Jinyang Du & Junguo Gao, 2019. "Design of Partial Discharge Test Environment for Oil-Filled Submarine Cable Terminals and Ultrasonic Monitoring," Energies, MDPI, vol. 12(24), pages 1-14, December.
    2. 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.
    3. Patryk Bohatyrewicz & Szymon Banaszak, 2022. "Assessment Criteria of Changes in Health Index Values over Time—A Transformer Population Study," Energies, MDPI, vol. 15(16), pages 1-15, August.
    4. Alexandra I. Khalyasmaa & Pavel V. Matrenin & Stanislav A. Eroshenko & Vadim Z. Manusov & Andrey M. Bramm & Alexey M. Romanov, 2022. "Data Mining Applied to Decision Support Systems for Power Transformers’ Health Diagnostics," Mathematics, MDPI, vol. 10(14), pages 1-25, July.
    5. Álvaro Jaramillo-Duque & Nicolás Muñoz-Galeano & José R. Ortiz-Castrillón & Jesús M. López-Lezama & Ricardo Albarracín-Sánchez, 2018. "Power Loss Minimization for Transformers Connected in Parallel with Taps Based on Power Chargeability Balance," Energies, MDPI, vol. 11(2), pages 1-12, February.
    6. Christian Gianoglio & Edoardo Ragusa & Paolo Gastaldo & Federico Gallesi & Francesco Guastavino, 2021. "Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks," Energies, MDPI, vol. 14(15), pages 1-23, August.
    7. Szymon Banaszak & Eugeniusz Kornatowski & Wojciech Szoka, 2021. "The Influence of the Window Width on FRA Assessment with Numerical Indices," Energies, MDPI, vol. 14(2), pages 1-18, January.
    8. Nuria Novas & Alfredo Alcayde & Isabel Robalo & Francisco Manzano-Agugliaro & Francisco G. Montoya, 2020. "Energies and Its Worldwide Research," Energies, MDPI, vol. 13(24), pages 1-41, December.
    9. Pedro J. Villegas & Juan A. Martín-Ramos & Juan Díaz & Juan Á. Martínez & Miguel J. Prieto & Alberto M. Pernía, 2017. "A Digitally Controlled Power Converter for an Electrostatic Precipitator," Energies, MDPI, vol. 10(12), pages 1-24, December.
    10. Christian Gianoglio & Edoardo Ragusa & Andrea Bruzzone & Paolo Gastaldo & Rodolfo Zunino & Francesco Guastavino, 2020. "Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus," Energies, MDPI, vol. 13(5), pages 1-16, March.
    11. Jing Wu & Kun Li & Jing Sun & Li Xie, 2018. "A Novel Integrated Method to Diagnose Faults in Power Transformers," Energies, MDPI, vol. 11(11), pages 1-8, November.
    12. Qing Yang & Peiyu Su & Yong Chen, 2017. "Comparison of Impulse Wave and Sweep Frequency Response Analysis Methods for Diagnosis of Transformer Winding Faults," Energies, MDPI, vol. 10(4), pages 1-16, March.
    13. Lei Peng & Qiang Fu & Yaohong Zhao & Yihua Qian & Tiansheng Chen & Shengping Fan, 2018. "A Non-Destructive Optical Method for the DP Measurement of Paper Insulation Based on the Free Fibers in Transformer Oil," Energies, MDPI, vol. 11(4), pages 1-9, March.
    14. Javier Leiva & Rubén Carmona Pardo & José A. Aguado, 2019. "Data Analytics-Based Multi-Objective Particle Swarm Optimization for Determination of Congestion Thresholds in LV Networks," Energies, MDPI, vol. 12(7), pages 1-20, April.
    15. 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.
    16. Michał Kunicki & Sebastian Borucki & Andrzej Cichoń & Jerzy Frymus, 2019. "Modeling of the Winding Hot-Spot Temperature in Power Transformers: Case Study of the Low-Loaded Fleet," Energies, MDPI, vol. 12(18), pages 1-17, September.
    17. Piotr Przybylek & Hubert Moranda & Hanna Moscicka-Grzesiak & Dominika Szczesniak, 2019. "Application of Synthetic Ester for Drying Distribution Transformer Insulation—The Influence of Cellulose Thickness on Drying Efficiency," Energies, MDPI, vol. 12(20), pages 1-16, October.
    18. Eugeniusz Kornatowski & Szymon Banaszak, 2019. "Frequency Response Quality Index for Assessing the Mechanical Condition of Transformer Windings," Energies, MDPI, vol. 13(1), pages 1-15, December.
    19. Alhaytham Alqudsi & Ayman El-Hag, 2019. "Application of Machine Learning in Transformer Health Index Prediction," Energies, MDPI, vol. 12(14), pages 1-13, July.
    20. Mehran Tahir & Stefan Tenbohlen, 2019. "A Comprehensive Analysis of Windings Electrical and Mechanical Faults Using a High-Frequency Model," Energies, MDPI, vol. 13(1), pages 1-25, December.

    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:14:y:2021:i:11:p:3227-:d:566627. 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.