Abnormality Detection of Cast-Resin Transformers Using the Fuzzy Logic Clustering Decision Tree

Chin-Tan Lee and Shih-Cheng Horng
Additional contact information
Chin-Tan Lee: Department of Electronic Engineering, National Quemoy University, Kinmen 892009, Taiwan
Shih-Cheng Horng: Department of Computer Science & Information Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan

Energies, 2020, vol. 13, issue 10, 1-19

Abstract: Failures of cast-resin transformers not only reduce the reliability of power systems, but also have great effects on power quality. Partial discharges (PD) occurring in epoxy resin insulators of high-voltage electrical equipment will result in harmful effects on insulation and can cause power system blackouts. Pattern recognition of PD is a useful tool for improving the reliability of high-voltage electrical equipment. In this work, a fuzzy logic clustering decision tree (FLCDT) is proposed to diagnose the PD concerning the abnormal defects of cast-resin transformers. The FLCDT integrates a hierarchical clustering scheme with the decision tree. The hierarchical clustering scheme uses splitting attributes to divide the data set into suspended clusters according to separation matrices. The hierarchical clustering scheme is regarded as a preprocessing stage for classification using a decision tree. The whole data set is divided by the hierarchical clustering scheme into some suspended clusters, and the patterns in each suspended cluster are classified by the decision tree. The FLCDT was successfully adopted to classify the aberrant PD of cast-resin transformers. Classification results of FLCDT were compared with two software packages, See5 and CART. The FLCDT performed much better than the CART and See5 in terms of classification precisions.

Keywords: cast-resin transformers; abnormal defects; partial discharge; pattern recognition; hierarchical clustering; decision tree (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/10/2546/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/10/2546/ (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:13:y:2020:i:10:p:2546-:d:359371

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 ().