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

Xiaoqiang Liu, Ji Li (), Lei Shao (), Hongli Liu, Lei Ren and Lihua Zhu
Additional contact information
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

Energies, 2023, vol. 16, issue 3, 1-14

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/3/1168/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/3/1168/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:3:p:1168-:d:1042809

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().