Transformer Winding Fault Classification and Condition Assessment Based on Random Forest Using FRA

Mehran Tahir () and Stefan Tenbohlen ()
Additional contact information
Mehran Tahir: Hochspannungstechnik & Transformatorbau GmbH (HTT), Veckerhäger Str. 100, 34346 Hannoversch Münden, Germany
Stefan Tenbohlen: Institute of Power Transmission and High Voltage Technology (IEH), Stuttgart University, Pfaffenwaldring 47, 70569 Stuttgart, Germany

Energies, 2023, vol. 16, issue 9, 1-16

Abstract: At present, the condition assessment of transformer winding based on frequency response analysis (FRA) measurements demands skilled personnel. Despite many research efforts in the last decade, there is still no definitive methodology for the interpretation and condition assessment of transformer winding based on FRA results, and this is a major challenge for the industrial application of the FRA method. To overcome this challenge, this paper proposes a transformer condition assessment (TCA) algorithm, which is based on numerical indices, and a supervised machine learning technique to develop a method for the automatic interpretation of FRA results. For this purpose, random forest (RF) classifiers were developed for the first time to identify the condition of transformer winding and classify different faults in the transformer windings. Mainly, six common states of the transformer were classified in this research, i.e., healthy transformer, healthy transformer with saturated core, mechanically damaged winding, short-circuited winding, open-circuited winding, and repeatability issues. In this research, the data from 139 FRA measurements performed in more than 80 power transformers were used. The database belongs to the transformers having different ratings, sizes, designs, and manufacturers. The results reveal that the proposed TCA algorithm can effectively assess the transformer winding condition with up to 93% accuracy without much human intervention.

Keywords: condition assessment; decision tree (DT); frequency response analysis (FRA); machine learning; numerical indices; power transformer; random forest (RF) (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: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/9/3714/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/9/3714/ (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:9:p:3714-:d:1133534

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 ().