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Transformer Winding Fault Classification and Condition Assessment Based on Random Forest Using FRA

Author

Listed:
  • Mehran Tahir

    (Hochspannungstechnik & Transformatorbau GmbH (HTT), Veckerhäger Str. 100, 34346 Hannoversch Münden, Germany
    These authors contributed equally to this work.)

  • Stefan Tenbohlen

    (Institute of Power Transmission and High Voltage Technology (IEH), Stuttgart University, Pfaffenwaldring 47, 70569 Stuttgart, Germany
    These authors contributed equally to this work.)

Abstract

At present, the condition assessment of transformer winding based on frequency response analysis (FRA) measurements demands skilled personnel. Despite many research efforts in the last decade, there is still no definitive methodology for the interpretation and condition assessment of transformer winding based on FRA results, and this is a major challenge for the industrial application of the FRA method. To overcome this challenge, this paper proposes a transformer condition assessment (TCA) algorithm, which is based on numerical indices, and a supervised machine learning technique to develop a method for the automatic interpretation of FRA results. For this purpose, random forest (RF) classifiers were developed for the first time to identify the condition of transformer winding and classify different faults in the transformer windings. Mainly, six common states of the transformer were classified in this research, i.e., healthy transformer, healthy transformer with saturated core, mechanically damaged winding, short-circuited winding, open-circuited winding, and repeatability issues. In this research, the data from 139 FRA measurements performed in more than 80 power transformers were used. The database belongs to the transformers having different ratings, sizes, designs, and manufacturers. The results reveal that the proposed TCA algorithm can effectively assess the transformer winding condition with up to 93% accuracy without much human intervention.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3714-:d:1133534
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Zbigniew Nadolny, 2023. "Design and Optimization of Power Transformer Diagnostics," Energies, MDPI, vol. 16(18), pages 1-7, September.
    2. 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.

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