Localization of Winding Shorts Using Fuzzified Neural Networks

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

研究成果: Article同行評審

30 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)140-146
頁數7
期刊IEEE Transactions on Energy Conversion
10
發行號1
DOIs
出版狀態Published - 1995 三月

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

指紋 深入研究「Localization of Winding Shorts Using Fuzzified Neural Networks」主題。共同形成了獨特的指紋。

引用此