Active Power Loss Reduction by Particle Swarm Optimization Algorithm

Authors

  • Lenin K Department of EEE Prasad V.Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh -520007, India

DOI:

https://doi.org/10.26438/ijcse/v7i1.904906

Keywords:

Optimal reactive power, Transmission loss, particle swarm optimization

Abstract

This work presents Particle swarm optimization (PSO) algorithm for solving optimal reactive power problem. PSO is an optimization tool based on a population, where each member is seen as a particle, and each particle is a potential solution to the problem under analysis. Each particle in PSO has a randomized velocity associated to it, which moves through the space of the problem. However, unlike genetic algorithms, PSO does not have operators, such as crossover and mutation. PSO does not implement the survival of the fittest individuals; rather, it implements the simulation of social behaviour. Projected Particle swarm optimization (PSO) algorithm has been tested in standard IEEE 300 bus system and simulation results show the better performance of the proposed algorithm in reducing the real power loss.

References

[1] O.Alsac, B. Scott, “Optimal load flow with steady state security”,IEEE Transaction. PAS -1973, pp. 745-751.

[2] Lee K Y ,Paru Y M , Oritz J L –A united approach to optimal real and reactive power dispatch , IEEE Transactions on power Apparatus and systems 1985: PAS-104 : 1147-1153

[3] A.Monticelli , M .V.F Pereira ,and S. Granville , “Security constrained optimal power flow with post contingency corrective rescheduling” , IEEE Transactions on Power Systems :PWRS-2, No. 1, pp.175-182.,1987.

[4] Deeb N ,Shahidehpur S.M ,Linear reactive power optimization in a large power network using the decomposition approach. IEEE Transactions on power system 1990: 5(2) : 428-435

[5] E. Hobson ,’Network consrained reactive power control using linear programming, ‘ IEEE Transactions on power systems PAS -99 (4) ,pp 868-877, 1980

[6] K.Y Lee ,Y.M Park , and J.L Oritz, “Fuel –cost optimization for both real and reactive power dispatches” , IEE Proc; 131C,(3), pp.85-93.

[7] M.K. Mangoli, and K.Y. Lee, “Optimal real and reactive power control using linear programming” , Electr.Power Syst.Res, Vol.26, pp.1-10,1993.

[8] Berizzi.C.Bovo,M.Merlo,andM.Delfanti,(2012), “A GA approach to compare ORPF objective functions including secondary voltage regulation,” Electric Power Systems Research, vol. 84, no. 1, pp. 187 – 194.

[9] D. Devaraj, and B. Yeganarayana, “Genetic algorithm based optimal power flow for security enhancement”, IEE proc-Generation.Transmission and. Distribution; 152, 6 November 2005.

[10] C.A. Canizares , A.C.Z.de Souza and V.H. Quintana , “ Comparison of performance indices for detection of proximity to voltage collapse ,’’ vol. 11. no.3 , pp.1441-1450, Aug 1996 .

[11] Kennedy JF, Eberhart RC. Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Perth, Australia, 1995. p. 1942–48.

[12] Eberhart RC, Kennedy JF. A new optimizer using particle swarm theory. In: Proceedings of international symposium on micro machine and human science, Japan, 1995. p. 39–43.

[13] Goldberg DE. Genetic algorithms in search, optimization, and machine learning. Reading (MA, USA): Addison Wesley; 1989.

[14] IEEE, “IEEE 118, 300 -test systems”, (1993), http://www.ee.washington.edu/trsearch/pstca/.

[15] S. Surender Reddy, “Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm”, International Journal of Electrical and Computer Engineering , Vol. 7, No. 5, pp. 2349-2356. 2017

[16] S.S. Reddy, et al., “Faster evolutionary algorithm based optimal power flow using incremental variables”, Electrical Power and Energy Systems, vol. 54, pp. 198-210, 2014.

Downloads

Published

2019-01-31
CITATION
DOI: 10.26438/ijcse/v7i1.904906
Published: 2019-01-31

How to Cite

[1]
K. Lenin, “Active Power Loss Reduction by Particle Swarm Optimization Algorithm”, Int. J. Comp. Sci. Eng., vol. 7, no. 1, pp. 904–906, Jan. 2019.

Issue

Vol. 7 No. 1 (2019): IJCSE January Edition

Section

Review Article

License

Creative Commons License

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

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.