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Fault Diagnosis of Transformer Windings Based on Decision Tree and Fully Connected Neural Network

Author

Listed:
  • ZhenHua Li

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
    Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang 443002, China)

  • Yujie Zhang

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China)

  • Ahmed Abu-Siada

    (Department of Electrical and Computer Engineering, Curtin University, Perth 6000, Australia)

  • Xingxin Chen

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China)

  • Zhenxing Li

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China)

  • Yanchun Xu

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China)

  • Lei Zhang

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China)

  • Yue Tong

    (China Electric Power Research Institute, Wuhan 430074, China)

Abstract

While frequency response analysis (FRA) is a well matured technique widely used by current industry practice to detect the mechanical integrity of power transformers, interpretation of FRA signatures is still challenging, regardless of the research efforts in this area. This paper presents a method for reliable quantitative and qualitative analysis to the transformer FRA signatures based on a decision tree classification model and a fully connected neural network. Several levels of different six fault types are obtained using a lumped parameter-based transformer model. Results show that the proposed model performs well in the training and the validation stages, and is of good generalization ability.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1531-:d:514340
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    References listed on IDEAS

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    1. Nanyan Zhu & Chen Liu & Andrew F. Laine & Jia Guo, 2020. "Understanding and Modeling Climate Impacts on Photosynthetic Dynamics with FLUXNET Data and Neural Networks," Energies, MDPI, vol. 13(6), pages 1-11, March.
    2. 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.
    3. Wei Zhang & Xiaohui Yang & Yeheng Deng & Anyi Li, 2020. "An Inspired Machine-Learning Algorithm with a Hybrid Whale Optimization for Power Transformer PHM," Energies, MDPI, vol. 13(12), pages 1-17, June.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.

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