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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
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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.
    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.
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    Citations

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    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.

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