Localization of Winding Shorts Using Fuzzified Neural Networks

M. A. El-Sharkawi, R. J. Marks, Seho Oh, S. J. Huang, Alonso Rodriguez, Isidor Kerszenbaum

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)


Shorted turns in field winding of large turbogenerators are difficult to detect and localize. We propose a technique whereby shorts are detected and localized using an artificial neural network with a fuzzified output. The method is based on injecting two simultaneous and identical waveform signals at both ends of the field winding. Selected features of the received signals are used to train the neural network. Once trained, the neural network can detect and localize short turns in the field winding. The proposed method is verified by a field test on 60 MVA turbogenerator. The results show that the proposed method is quite accurate and efficient.

Original languageEnglish
Pages (from-to)140-146
Number of pages7
JournalIEEE Transactions on Energy Conversion
Issue number1
Publication statusPublished - 1995 Mar

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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