CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Performance Comparison of Invasive Weed Optimization and Particle Swarm Optimization Algorithm for the tuning of Power System Stabilizer in Multi-machine Power System

by Ashik Ahmed, B. M. Ruhul Amin
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 41 - Number 16
Year of Publication: 2012
Authors: Ashik Ahmed, B. M. Ruhul Amin
10.5120/5626-7943

Ashik Ahmed, B. M. Ruhul Amin . Performance Comparison of Invasive Weed Optimization and Particle Swarm Optimization Algorithm for the tuning of Power System Stabilizer in Multi-machine Power System. International Journal of Computer Applications. 41, 16 ( March 2012), 29-36. DOI=10.5120/5626-7943

@article{ 10.5120/5626-7943,
author = { Ashik Ahmed, B. M. Ruhul Amin },
title = { Performance Comparison of Invasive Weed Optimization and Particle Swarm Optimization Algorithm for the tuning of Power System Stabilizer in Multi-machine Power System },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 41 },
number = { 16 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 29-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume41/number16/5626-7943/ },
doi = { 10.5120/5626-7943 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:29:46.094575+05:30
%A Ashik Ahmed
%A B. M. Ruhul Amin
%T Performance Comparison of Invasive Weed Optimization and Particle Swarm Optimization Algorithm for the tuning of Power System Stabilizer in Multi-machine Power System
%J International Journal of Computer Applications
%@ 0975-8887
%V 41
%N 16
%P 29-36
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, two evolutionary algorithms- Invasive Weed Optimization (IWO) based power system stabilizer (PSS) and particle swarm optimization (PSO) based power system stabilizer is designed for multi-machine power system to compare their tuning performances. IWO is a derivative-free real parameter optimization technique that mimics the ecological behavior of colonizing weeds. PSO is also a derivative-free and flexible optimizer which is powered by the behavior of organism, such as bird flocking. Eigen-value based objective function is considered for the tuning of PSSs to enhance system damping of electromechanical mode. The performance of proposed IWO-based PSS and PSO-based PSS is tested and demonstrated under different disturbances for a four machine example power system. The Eigen value analysis and non-linear time domain simulation results shows that both IWO-based PSS and PSO-based design can successfully damp out the oscillations and thus improve the stability of the system. However, the abilities like faster convergence and greater shifting of critical modes to the left of s-plane keeps the choice of IWO based design in front of PSO based design for the system under consideration.

References
  1. Padiyar K. R. , Power System Dynamics Stability and Control', Anshan Publishers, 2nd edition.
  2. Anderson P. M. , Fouad, A. A. , Power System Control and Stability, IEEE series on power engineering, 2nd edition.
  3. Larsen E. and Swann D. , Applying power system stabilizers, IEEE Trans. Power App. Systems, Vol. PAS-100, 1981, pp. 3017-3046.
  4. Cao Y. , Jiang L. , Cheng S. , Chen D. , Malik O. P. and Hope G. S. , " A nonlinear variable structure stabilizer for power system stability", IEEE Trans. Energy Conversion, vol. 9, Sept. 1994,pp. 489–495.
  5. Hiyama T. and Sameshima T. , "Fuzzy logic control scheme for on-line stabilization of multimachine power system," Fuzzy Sets Sys. , vol. 39, 1991 pp. 181–194.
  6. Abido M. A. and Abdel-Magid Y. L. , "A hybrid neuro-fuzzy power system stabilizer for multi-machine power systems," IEEE Trans. Power Syst. , vol. 13, Nov. 1998pp. 1323–1330.
  7. D. Xia and G. T. Heydt, "Self-tuning controller for generator excitation control," IEEE Trans. Power App. Syst. , 1983, pp. 1877–1885.
  8. P. Kundur, M. Klein, G. J. Rogers, and M. S. Zywno, Application of power system stabilizers for enhancement of overall system stability, IEEE Trans. on Power Systems. Vol. PAS-108, 1989, pp. 614-626.
  9. R. J. Fleming, M. A. Mohan, K Parvatism," Selection of parameters of stabilizers in multi-machine power systems", IEEE Trans, PAS, vol. 100, no. 5,1981, pp. 2329-2333.
  10. Mahdiyeh Eslami, Hussain Shareef and Azah Mohamed, "Optimal tuning of power system stabilizers using modified particle swarm optimization", Proceedings of the 14th International Middle East Power Systems Conference (MEPCON'10), Cairo University, Egypt, December 19-21, 2010.
  11. M. A. Abido, "A novel approach to conventional power system stabilizer design using tabu search," Int. J. Electr. Power Energy Syst. , vol. 21, 1999, pp. 443-454.
  12. Y. L. Abdel-Magid and M. A. Abido, "Optimal multiobjective design of robust power system stabilizers using genetic algorithms," IEEE Transactions on Power Systems, vol. 18, 2003,pp. 1125-1132.
  13. A. L. B. Do Bomfim, et al. , "Simultaneous tuning of power system damping controllers usinggenetic algorithms," IEEE Trans. Power Syst. ,vol. 15,2000, pp. 163-169.
  14. M. A. Abido, Robust design of multi-machine power system stabilizers using simulated annealing, IEEE Trans. on Energy Conversion, Vol. 15, No. 3, 2003, pp. 297-304.
  15. M. A. Abido, Y. L. Abdel-Magid, Optimal design of power system stabilizers using evolutionary programming, IEEE Trans. on Energy Conversion, Vol. 17, No. 4,2002, pp. 429-436.
  16. P. Zhang and A. H. Coonick, "Coordinated synthesis of PSS parameters in multi-machine powersystems using the method of inequalities applied to genetic algorithms," IEEE Trans. Power Syst. , vol. 15, 2000, pp. 811-816.
  17. A. R. Mehrabian, C. Lucas, A novel Numerical Optimization Algorithm Inspired from Weed Colonization, Ecological Informatics, 2006,vol 1. pp-355-366.
  18. A. R. Mehrabian, A. Yousefi-Koma, Optimal Positioning of Piezoelectric Actuators on a Smart Fin using Bio-inspired Algorithms, Aerospace Science and Technology,2007,vol 11, pp 174-182.
  19. A. R. Mallahzadeh ,S. Es'haghi, A Alipour, " Design of an E shaped MIMO Antenna using IWO Algorithm for Wireless Application at 5. 8 Ghz", Progress in Electromagnetic Research,PIER 90, 2009,187-203.
  20. X. Zhang, Y. Wang, G. Cui, Y. Niu, J. Xu, Application of a novel IWO to the design of encoding sequence for DNA computing, Comput. Math. Appl. 57, Jun 2009, pp. 2001-2008,
  21. H. Sepehri Rad , C. Lucas, " A Recommender System based on Invasive Weed Optimization Algorithm", IEEE Congress on Evolutionary Computation, CEC 2007,pp 4297-4304.
  22. M. Shaheri-Ardakani, M. Rshanaei, A. Rahimi-Kian, C. Lucas, "A study of electricity market dynamics using Invasive Weed Colonization Optimization," in Proc. IEEE Symp. Comput. Intell. Games, 2008, pp. 276-282.
  23. P. W. Sauer and M. A. Pai, Power System Dynamics and Stability. (Englewood Cliffs, NJ: Prentice–Hall, 1998. )
  24. J. Kennedy and R. C. Eberhart, "Particle swarm optimization," in Proc. IEEE Int. Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948, 1995.
  25. Y. Shi and R. C. Eberhart, "A modified particle swarm optimizer," in Proc. IEEE Int. Conference on Evolutionary Computation, Piscataway, NJ, pp 69–73, 1998.
  26. M. Meissner, M. Schmuker, and G. Schneider, "Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training," BMC Bioinformatics, vol. 7, no. 125, 2006.
  27. Z. Cui1, J. Zeng, and G. Sun, "A Fast Particle Swarm Optimization," Int. J. of Innovative Computing, Information and Control, vol. 4, no. 6, pp. 1365–1380, 2006.
  28. S. Karimkashi, Ahmed A. Kishk, "Invasive Weed Optimization and its Features in Electromagnetics", IEEE Transactions on Antenna and Propagation, Vol. 58, No. 4, April 2010, pp. 1269-1278.
  29. Kundur P. , Power System Stability And Control (McGraw-Hill Press, 1994).
Index Terms

Computer Science
Information Sciences

Keywords

Pss Design Invasive Weed Optimization Particle Swarm Optimization Dynamic Stability

Powered by PhDFocusTM