LOCALIZATION OF WINDING SHORTS USING FUZZIFIED NEURAL NETWORKS

被引：25

作者：

ELSHARKAWI, MA ^{[1
]}

MARKS, RJ ^{[1
]}

OH, S ^{[1
]}

HUANG, SJ ^{[1
]}

KERSZENBAUM, I ^{[1
]}

RODRIGUEZ, A ^{[1
]}

机构：

[1] SO CALIF EDISON CO,RES CTR,ROSEMEAD,CA

来源：

IEEE TRANSACTIONS ON ENERGY CONVERSION | 1995年 / 10卷 / 01期

关键词：

SYNCHRONOUS MACHINE; SHORT TURNS; NEURAL NETWORKS;

D O I：

10.1109/60.372579

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

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.

引用

页码：140 / 146

页数：7

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