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Volume 16
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10.3390/en16031168
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Article

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

by
Xiaoqiang Liu
1,
Ji Li
2,*,
Lei Shao
2,*,
Hongli Liu
2,
Lei Ren
2 and
Lihua Zhu
2
1
School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China
2
Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China
*
Authors to whom correspondence should be addressed.
Energies 2023, 16(3), 1168; https://doi.org/10.3390/en16031168
Submission received: 26 December 2022 / Revised: 11 January 2023 / Accepted: 18 January 2023 / Published: 20 January 2023
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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.
Keywords: vibration features; hierarchical clustering; decision trees; fault early warning vibration features; hierarchical clustering; decision trees; fault early warning

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MDPI and ACS Style

Liu, X.; Li, J.; Shao, L.; Liu, H.; Ren, L.; Zhu, L. Transformer Fault Early Warning Analysis Based on Hierarchical Clustering Combined with Decision Trees. Energies 2023, 16, 1168. https://doi.org/10.3390/en16031168

AMA Style

Liu X, Li J, Shao L, Liu H, Ren L, Zhu L. Transformer Fault Early Warning Analysis Based on Hierarchical Clustering Combined with Decision Trees. Energies. 2023; 16(3):1168. https://doi.org/10.3390/en16031168

Chicago/Turabian Style

Liu, Xiaoqiang, Ji Li, Lei Shao, Hongli Liu, Lei Ren, and Lihua Zhu. 2023. "Transformer Fault Early Warning Analysis Based on Hierarchical Clustering Combined with Decision Trees" Energies 16, no. 3: 1168. https://doi.org/10.3390/en16031168

APA Style

Liu, X., Li, J., Shao, L., Liu, H., Ren, L., & Zhu, L. (2023). Transformer Fault Early Warning Analysis Based on Hierarchical Clustering Combined with Decision Trees. Energies, 16(3), 1168. https://doi.org/10.3390/en16031168

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