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

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 trans...

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Bibliographic Details
Published in:IEEE transactions on power delivery 2005-10, Vol.20 (4), p.2389-2395
Main Authors: Gracia, J., Mazon, A.J., Zamora, I.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial neural networks
Artificial neural networks (ANNs)
Circuit faults
Classification
Computer programs
Data acquisition
Disturbances. Regulation. Protection
Electric variables measurement
Electric, optical and optoelectronic circuits
Electrical engineering. Electrical power engineering
Electrical power engineering
Electronics
Exact sciences and technology
fault classification
Fault location
Faults
Learning theory
learning vector quantization
multilayer perceptron
Neural networks
Power networks and lines
Power transmission lines
Pulse measurements
Transmission lines
Vector quantization
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Summary: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.
ISSN:0885-8977
1937-4208
DOI:10.1109/TPWRD.2005.855482