Strategy based on genetic algorithms for an optimal adjust of a support vector machine used for locating faults in power distribution systems
Keywords:
classification, fault location, genetic algorithms, power distribution systems and support vector machinesAbstract
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%.
Downloads
References
IEEE Std 37.114. IEEE Guide for Determining Fault Location on AC Transmission and Distribution Lines. Power System Relaying Committee. 2004. pp. 1-36.
T. Short. Electric Power Distribution Handbook. CRC press. New York. Vol. 1. 2003. pp. 8-30.
J. Mora, G. Carrillo, B. Barrera. “Fault Location in Power Distribution Systems Using a Learning Algorithm for Multivariable Data Analysis”. IEEE Transaction on Power Delivery.Vol. 22. 2007. pp. 1715-1721.
J. Mora-Flórez, J. Meléndez, G. Carrillo-Caicedo, “Comparison of impedance based fault location methods for power distribution systems,” Electric Power Systems Research. Vol. 78. 2008. pp. 657-666.
K. Srinivasan, A. St-Jacques. “A new fault location algorithm for radial transmission lines with loads”. IEEE Transactions on Power Delivery. Vol. 4. 1989. pp. 1676-1682.
A. Girgis, C. Fallon, D. Lubkeman. “A fault location technique for rural distribution feeders” IEEE Transactions on Industry and Applications. Vol. 26. 1993. pp. 1170-1175.
A. Mescal, A. Al-Shaher, S. Manar. “Fault location in multi-ring distribution network using artificial neural network”. Electric Power Systems Research. Vol 64. 2003. pp. 87-92.
D. Thukaram, H. Khincha H. Vijaynarasimha “Artificial Neural Network and Support Vector Machine Approach for Locating Faults in Radial Distribution Systems”. IEEE Transactions on Power Delivery.Vol. 20. 2005. pp. 71 721.
G. Morales, J. Mora, S. Pérez. “Learning-based strategy for reducing the multiple estimation problem of fault zone location in radial power Systems” IET Generation, Transmission and Distribution. Vol. 3. 2009. pp. 346-356.
C. Burges. A tutorial on Support Vector Machines for Pattern Recognition. http://www.kernel-machines. org/. Consultada el 15 de septiembre de 2008.
S. Bernhard, S. Alex. Learning with Kernels Support Vector Machines, Regularization, Optimization and Beyond. Ed. MIT Press. Cambridge. 2002. pp. 24-69.
A. Eiben, J. Smith. Introduction to Evolutionary Computing (Natural Computing Series). Ed. Springer Verlag. Heidelberg. 2003. pp. 37-69.
S. Sivanandam, S. Deepa. Introduction to Genetic Algorithms. Springer Verlag. Heidelberg. 2003. pp. 19-78.
P. Anderson. Analysis of faulted power systems. Ed. The Iowa State University Press. NewYork. 1995. pp. 56-127.
J. Mora. “Localización de Fallas en Sistemas de Distribución de Energía Eléctrica usando Métodos Basados en el Modelo y Métodos Basados en el Conocimiento”. Tesis Doctoral. University of Girona. España. 2006. pp. 81-86.
IEEE Distribution System Analysis Subcommittee. Radial Test Feeders. IEEE Standards Board. (http:// www.ewh.ieee.org/soc/pes/dsacom/testfeeders.html). 1993. Consultada el 08 de mayo de 2009.
J. Dagenhart. “The 40-Ground-Fault Phenomenon”. IEEE Transactions on Industry Applications. Vol. 36. 2000. pp. 30-32.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2018 Revista Facultad de Ingeniería
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Revista Facultad de Ingeniería, Universidad de Antioquia is licensed under the Creative Commons Attribution BY-NC-SA 4.0 license. https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
You are free to:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
NonCommercial — You may not use the material for commercial purposes.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
The material published in the journal can be distributed, copied and exhibited by third parties if the respective credits are given to the journal. No commercial benefit can be obtained and derivative works must be under the same license terms as the original work.
Twitter