Title :
Best ANN structures for fault location in single-and double-circuit transmission lines
Author :
Gracia, J. ; Mazón, A.J. ; Zamora, I.
Author_Institution :
Gov. of the Autonomous Community of Aragon, Zaragoza, Spain
Abstract :
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.
Keywords :
fault location; neural nets; power engineering computing; power transmission faults; ANN structures; SARENEUR software tool; artificial neural nets; fault classification; fault location; single-circuit transmission lines; Artificial neural networks; Circuit faults; Data acquisition; Electric variables measurement; Fault location; Neural networks; Power transmission lines; Pulse measurements; Transmission lines; Vector quantization; Artificial neural networks (ANNs); fault classification; fault location; learning vector quantization; multilayer perceptron;
Journal_Title :
Power Delivery, IEEE Transactions on
DOI :
10.1109/TPWRD.2005.855482
