GENETIC ALGORITHM BASED LOAD FLOW SOLUTION PROBLEM IN ELECTRICAL POWER SYSTEMS

Main Article Content

Hassan A. Kubba
Samir Sami Mahmood

Abstract

In this paper, a proposed method based on real-coded genetic algorithm is presented and applied to solve multiple load flow solution problem. Genetic algorithm is a kind of stochastic search algorithm based on the mechanics of natural selection and natural genetics. They combine the concepts of survival of the fittest with genetic operators such as selection, crossover and mutation abstracted from nature to form a surprisingly robust mechanism that has been successfully applied to solve a variety of search and optimization problems. Elitist method is also used in this research, and blending models are implemented for crossover operator. In the proposed work, five busbars typical test system and 362-bus Iraqi National Grid are used to demonstrate the efficiency and performance of the proposed method. The results show that, genetic algorithm is on-line load flow solution problem for small-scale power systems, but for large-scale power systems, it is recommended that the load flow solution using genetic algorithm is for planning studies. The main important feature of the purposed method is to give high accurate solution with respect to the conventional methods

Article Details

How to Cite
“GENETIC ALGORITHM BASED LOAD FLOW SOLUTION PROBLEM IN ELECTRICAL POWER SYSTEMS” (2009) Journal of Engineering, 15(04), pp. 4142–4162. doi:10.31026/j.eng.2009.04.04.
Section
Articles

How to Cite

“GENETIC ALGORITHM BASED LOAD FLOW SOLUTION PROBLEM IN ELECTRICAL POWER SYSTEMS” (2009) Journal of Engineering, 15(04), pp. 4142–4162. doi:10.31026/j.eng.2009.04.04.

Publication Dates

References

 Abdul-Haleem G. F., 2005, “A Genetic Algorithm for Manufacturing Cell Formation”, M.Sc Thesis, Mechanical Department, University of Baghdad.

 Al-Shakarchi M. R. G., 1973, “Nodal Iterative Load Flow”, A dissertation submitted to the Victoria University of Manchester.

 AL-BAKRI A. A., 1994, “A Study of Some Problems on the Iraqi National Grid and Establishing a Method Algorithm for Load Flow”, M.Sc. Thesis, University of Baghdad.

 Grisby Leonard L., 2007, “Power Systems”, CRC Press.

 Haupt R. L. and Haupt S. E., 2004, “Practical Genetic Algorithms”, A John Wiley & Sons, INC., Publication, 2nd edition.

 Holland J., 1975, “Adaptation in Natural and Artificial Systems”, MIT Press.

 Ibrahim S. B. M., 2005, “The PID Controller Design Using Genetic Algorithm”, A dissertation submitted to University of Southern Queensland, Faculty of engineering and surveying, Electrical and Electronics Engineering.

 Kubba H.A., 1987, “Comparative Study of Different Load Flow Solution Methods”, M.Sc Thesis, University of Baghdad.

 Michalewicz Z., 1996, “Genetic Algorithms + Data Structure = Evolution Programs”, AI series, Springer-Verlag, New York, 3rd edition.

 Nanda J., Kothari D. P. and Srivastava S. C., 1987, “Some Important Observations on Fast Decoupled Load Flow Algorithm”, Proceedings of the IEEE, VOL. 75, No. 5, 732-733

 Pohlheim H., 2005, “GEATBx: Genetic and Evolutionary Algorithm Toolbox for Use with Matlab”, available at http://www.geatbx.com/.

 Stott B. and Alsac O., 1974, “Fast Decoupled Load Flow”, IEEE Trans. Power App. Syst., VOL. PAS-93, 859-869

 STAGG G. W. and AL-ABIAD A., 1968, “Computer Methods in Power System Analysis”, Mc-Graw Hill Publishing Company.

 Talib A., 2007, “An Optimization Approach of Robot Motion Planning Using Genetic Algorithm”, M.Sc Thesis, Mechatronics Department, AL-Khwarizmi Engineering, University of Baghdad.

 Taylor D. G. and Treece J. A., 1967, “Load Flow Analysis by Gauss-Seidel Method”, presented at the Symp. on power systems load flow analysis, University of Manchester Institute of Science and Technology, Manchester, U.K.

 Woon L. C., 2004, “Genetic Algorithm for Load Flow Solution Techniques”, Abstract of thesis, Master of engineering (electrical), http://www.sps.utm.

 Younes M. and Rahli M., 2006, “On the Choice Genetic Parameters with Taguchi Method Applied in Economic Power Dispatch”, Leonardo journal of sciences, issue 9, pp. 9-24.

 Zamanan N., Sykulski J., Al-Othman A. K., 2006, “A Digital Technique for Online Identification and Tracking of Power System Harmonics Based on Real Coded Genetic Algorithm”, Proceedings of the sixth IASTED international conference, European power and energy systems, Rhodes, Greece.

Similar Articles

You may also start an advanced similarity search for this article.