Multi-objective artificial bee colony algorithm to estimate transformer equivalent circuit parameters

Zuleyha Yilmaz, Musab Oksar, Fatih Basciftci

Abstract


Real world problems such as scientific, engineering, industrial problems are in the form of the multi-objective optimization problems. In order to achieve optimum solutions of such problems, multi-objective optimization algorithms are utilized. In this study, the problem is estimation of single-phase transformer parameters which is one of the engineering problems. This estimation is provided by artificial bee colony (ABC) algorithm. ABC is developed as a metaheuristic method and simulates foraging of bees. Since the problem is a multi-objective optimization problem, multi-objective ABC (MOABC) is proposed to estimate parameters in the study. This study aims to estimate equivalent circuit parameters using current and voltage values at any known load. Through algorithm, difference between actual and estimated parameter values that is the error has been tried to minimize. The successful results show that the proposed method can be used for a single-phase transformer parameters estimation.

Keywords


Multi-objective artificial bee colony algorithm; Multi-objective optimization; Transformer parameter estimation

Full Text:

PDF

References


K. Deb, Multi-Objective optimization using evolutionary algorithms, John Wiley & Sons, Inc. New York, NY, USA, 2001.

T. Murata, H. Ishibuchi, and H. Tanaka, “Multi-objective genetic algorithm and its applications to flowshop scheduling,” Computers & Industrial Engineering, vol. 30, pp. 957-968, 1996.

H. Ishibuchi and T. Yamamoto, “Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining,” Fuzzy sets and systems, vol. 141(1), pp. 59-88, 2004.

G. Zhang, X. Shao, P. Li, and L. Gao, “An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem,” Computers & Industrial Engineering, vol. 56 (4), pp. 1309-1318, 2009.

S. Chamaani, S. A. Mirtaheri, M. Teshnehlab, and M. A. Shooredeli, “Modified multi-objective particle swarm optimization for electromagnetic absorber design,” In Applied Electromagnetics, 2007. APACE 2007. Asia-Pacific Conference on IEEE, pp. 1-5, December, 2007.

D. Meister and M. A. G. de Oliveira, "The use of the least squares method to estimate the model parameters of a transformer," 2009 10th International Conference on Electrical Power Quality and Utilisation, Lodz, doi: 10.1109/EPQU.2009.5318853, pp. 1-6, 2009.

S. A. Soliman, R. A. Alammari, and M. A. Mostafa, "On-line estimation of transformer model parameters," 2004 Large Engineering Systems Conference on Power Engineering (IEEE Cat. No.04EX819), doi: 10.1109/LESCPE.2004.1356295, pp. 170-178, 2004.

S. H. Thilagar and G. S. Rao, "Parameter estimation of three-winding transformers using genetic algorithm," Eng. Appl. Artificial Intell., vol. 15, no. 5, pp. 429-437, Sep. 2002.

K. Deb, “A fast and elitist multiobjective genetic algorithm: NSGA-II”, IEEE Transactions On Evolutionary Computation, vol. 6, pp. 182-197, 2002.

A. H. F. Dias and J. A. de Vasconcelos, “Multiobjective genetic algorithms applied to solved optimization problems,” IEEE Transactions On Magnetics, vol. 38, no. 2, pp. 1133-1136, Mar. 2002.

A. Osyczka. “Evolutionary algorithms for single and multicriteria design optimization,” New York: Physica Verlag., 2002.

W. Zou, Y. Zhu, H. Chen, and B. Zhang, “Solving multiobjective optimization problems using artificial bee colony algorithm,” Discrete Dynamics in Nature and Society, vol. 2011, 37 pages, 2011.

J. D. Knowles and D. W. Corne, “Approximating the nondominated front using the pareto archived evolution strategy,” Evolutionary Computation, vol. 8(2), pp. 149-172, 2000.

C. A. Coello and G. T. Pulido, “A micro-genetic algorithm for multiobjective optimization,” in Proc. EMO 2001, pp. 126-140, Mar. 2001.

D. Karaboga, An Idea Based On Honey Bee Swarm for Numerical Optimization, Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.

S. J. Chapman, Electric Machinery Fundamentals, McGraw-Hill, New York, 2003.




DOI: http://dx.doi.org/10.21533/pen.v5i3.103

Refbacks

  • There are currently no refbacks.


Copyright (c) 2017 Periodicals of Engineering and Natural Sciences (PEN)

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License