Analysis of power transformer dissolved gas data using the self-organizing map

K F Thang, R K Aggarwal, A J McGrail, D G Esp

Research output: Contribution to journalArticle

75 Citations (Scopus)

Abstract

Incipient faults in power transformers can degrade the oil and cellulose insulation, leading to the formation of dissolved gases. Even though established approaches that relate these dissolved gas information to the condition of power transformers are already developed, it is discussed in this paper that they still contain some limitations. In view of that, this paper introduces an alternative approach for the analysis of dissolved gas data, which can produce more convincing interpretation and fault diagnosis. The proposed approach, which is based on the data mining methodology and the self-organizing map, has been compared and validated using conventional interpretation schemes and real fault-cases, thereby proven to be capable of enhancing the condition monitoring of power transformers.
Original languageEnglish
Pages (from-to)1241-1248
Number of pages8
JournalIEEE Transactions on Power Delivery
Volume18
Issue number4
Publication statusPublished - 2003

Keywords

  • data mining
  • chemical analysis
  • incipient faults
  • cellulose insulation
  • power engineering computing
  • self-organising feature maps
  • oil insulation
  • power transformer insulation
  • fault diagnosis
  • transformer oil
  • condition monitoring
  • self-organizing map
  • power transformers
  • dissolved gas analysis

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