Best ANN structures for fault location in single-and double-circuit transmission lines

被引：83
|
作者：
Gracia, J [1 ]
Mazón, AJ
Zamora, I
机构：
[1] Govt Autonomous Community Aragon, Zaragoza 50015, Spain
[2] Univ Basque Country, Dept Elect Engn, ETSI Bilbao, Bilbao 48013, Spain
来源：
IEEE TRANSACTIONS ON POWER DELIVERY | 2005年 / 20卷 / 04期
关键词：
artificial neural networks (ANNs); fault classification; fault location; learning vector quantization; multilayer perceptron;
D O I：
10.1109/TPWRD.2005.855482
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The great development in computing power has allowed the implementation of artificial neural networks (ANNs) in the most diverse fields of technology. This paper shows how diverse ANN structures can be applied to the processes of fault classification and fault location in overhead two-terminal transmission lines, with single and double circuit. The existence of a large group of valid ANN structures.guarantees the applicability of ANNs in the fault classification and location processes. The selection of the best ANN structures for each process has been carried out by means of a software tool called SARENEUR.
引用
收藏
页码：2389 / 2395
页数：7
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