Strategy based on genetic algorithms for an optimal adjust of a support vector machine used for locating faults in power distribution systems

Strategy based on genetic algorithms for an optimal adjust of a support vector machine used for locating faults in power distribution systems

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This paper presents a hybrid alternative to obtain a low computational cost strategy used to adjust the parameters of a Support Vector Machine based fault locator. The proposed strategy to determine the best parameters is based on the Chu Beasley Genetic Algorithm. The fault locator is tested in th...

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Bibliographic Details
Main Authors: Jaime Gutiérrez Gallego, Juan Mora Flórez, Sandra Pérez Londoño
Format: Article
Language:English
Published: Universidad de Antioquia 2013-03-01
Series:Revista Facultad de Ingeniería Universidad de Antioquia
Subjects:
classification
fault location
genetic algorithms
power distribution systems and support vector machines
Online Access:https://revistas.udea.edu.co/index.php/ingenieria/article/view/14788
Description
Summary:This paper presents a hybrid alternative to obtain a low computational cost strategy used to adjust the parameters of a Support Vector Machine based fault locator. The proposed strategy to determine the best parameters is based on the Chu Beasley Genetic Algorithm. The fault locator is tested in the IEEE 34 bus feeder, using a database of 2,180 registers of single phase, phase to phase, double phase to ground and three phase faults, obtained from simulation in ATP and Matlab. As results, the best alternatives for all of these four types of faults give an average cross validation error of 0.3%.
ISSN:0120-6230
2422-2844

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