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Transformer Fault Early Warning Analysis Based on Hierarchical Clustering Combined with Decision Trees

Author

Listed:
  • Xiaoqiang Liu

    (School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China)

  • Ji Li

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

  • Lei Shao

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

  • Hongli Liu

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

  • Lei Ren

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

  • Lihua Zhu

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

Abstract

The issues of low accuracy, poor generality, high cost of transformer fault early warning, and the subjective nature of empirical judgments made by field maintenance personnel are difficult to solve with the traditional measurement methods used during the development of the transformer. To construct a transformer fault early warning analysis, this study recommends a data-fusion-based decision tree approach for merging electrical quantity signals with a non-electrical amount of vibration signals. By merging a decision tree inference with actual operation data, a clustering center, and an early warning model, this method creates a transformer fault early warning model with self-learning ability and adaptive capabilities. After reasonable verification, the method becomes more universal and interpretable, and it can successfully conduct an early warning of transformer faults.

Suggested Citation

  • Xiaoqiang Liu & Ji Li & Lei Shao & Hongli Liu & Lei Ren & Lihua Zhu, 2023. "Transformer Fault Early Warning Analysis Based on Hierarchical Clustering Combined with Decision Trees," Energies, MDPI, vol. 16(3), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1168-:d:1042809
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    References listed on IDEAS

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