Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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A Novel Algorithm for Optimal Location of FACTS Devices in Power System Planning

  • Published : 2008.06.30
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Abstract

The particle swarm optimization(PSO) has been shown to converge rapidly during the initial stages of a global search, but around global optimum, the search process becomes very slow. On the other hand, the genetic algorithm is very sensitive to the initial population. In fact, the random nature of the GA operators makes the algorithm sensitive to initial population. This dependence to the initial population is in such a manner that the algorithm may not converge if the initial population is not well selected. In this paper, we have proposed a new algorithm which combines PSO and GA in such a way that the new algorithm is more effective and efficient and can find the optimal solution more accurately and with less computational time. Optimal location of SVC using this hybrid PSO-GA algorithm is found. We have also found the optimal place of SVC using GA and PSO separately and have compared the results. It has been shown that the new algorithm is more effective and efficient. An IEEE 68 bus test system is used for simulation.

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References

  1. A.A. Alabduljabbar and Milanovich, "Genetic algorithm for allocation of static VAr compensators", The 8th IEE International Conference on AC and DC Power Transmission, 2006. ACDC 2006. Pages: 115-120, 28-31 March 2006
  2. N.G. Hingorani and L. Gyugyi, "Understanding FACTS concepts and technology of flexible AC transmission systems", IEEE press 2000, ISBN 0-7803-3455-8
  3. R.M. Mathur and R.K. Varma, "Thyristor based FACTS controllers for electrical transmission systems", John Wily & Sons Inc. 2002
  4. A .A. Edris, R. Aapa, M.H. Baker, L. Bohman, K. Clark, "Proposed terms and definitions for flexible ac transmission system (FACTS)", IEEE Tran. on power delivery Vol. 12, No. 4, Oct. 1997
  5. V. K. Chandrakar, A.G. Kothari, "Optimal Location for Line Compensation by Shunt Connected FACTS Controller" The Fifth International Conference on Power Electronics and Drive Systems, PEDS 2003, Vol. 1, Page(s): 151- 156, 17-20 Nov. 2003
  6. Betram Koh Lin Hon, "Accelerated Genetic Algorithm in Power System Planning" Electrical Engineering Thesis, 2003
  7. A. Kazemi and B. Badrzadeh, "Modeling & Simulation of SVC and TCSC to study their limits on maximum loadability point", International Journal on Electric Power & Energy systems Vol. 26. Pages: 619-626, April 2004 https://doi.org/10.1016/j.ijepes.2004.04.008
  8. M. Saravanan, S. Mary Raja Slochanal, P. Venkatesh, Prince Stephen Abraham. J, "Application Of PSO Technique For Optimal Location Of FACTS Devices Considering System Loadability And Cost Of Installation", Electric Power System Research of Elsevior , March 2006
  9. Dussan Poveh, "Modeling of FACTS in power system studies", IEEE Power Engineering Society Winter Meeting, Vol. 2. Pages: 1435-1439, January 2000
  10. Stephane Gerbex, Richard Cherkaoui and Alain.J.Germond" Optimal location of multi-type FACTS devices by means of Genetic algorithm", IEEE Trans. Power System, Vol. 16. Pages: 537-544, August 2001 https://doi.org/10.1109/59.932292
  11. T.T. Ma, "Enhancement of power transmission systems by using Multiple UPFC on Evolutionary programming" IEEE Bologna Power Tech conference, Vol. 14. June 2003
  12. Mori, H.; Goto, Y, "A parallel tabu search based method for determining optimal allocation of FACTS in power systems", IEEE Proceedings. PowerCon 2000. International Conference on Power System Technology
  13. Bhasaputra, P.; Ongsakul, W, "Optimal placement of multi-type FACTS devices by hybrid TS/SA approach", IEEE 2003. ISCAS apos; 03. Proceedings of the 2003 International Symposium on Circuits and Systems
  14. James Kennedy and Russel Eberhart, "Particle Swarm Optimization", Proc. of IEEE International conference on neural networks, Vol. 14. Pages: 1942- 1948. December 1995
  15. Yuhui Shi, Russel.C.Eberhart, "Emperical study of particle swarm optimization", Proc. of the congress on Evolutionary computation, Vol.13. Pages: 1945-1950, July 1999
  16. Jing-Ru Zhang, Jun Zhang, Tat-Ming Lok, Michael R. Lyu, "A hybrid particle swarm optimization-backpropagation algorithm for feedforward neural network training", Elsevier 2006, Applied Mathematics and Computation
  17. M. Moghawemi, M.O. Faruque, "Effects of Facts Devices on Static Voltage Stability" TENCON 2000. Proceedings. Page(s):357- 362. Vol. 2, 2000
  18. J.H. Holland, Adaptation in Natural and Artificial Systems, Ann Arbor, MI: The University of Michigan Press, 1975
  19. Jose Miva and Jose Ramon Alvarez, "Artificial Intelligence and Knowledge Engineering Applications" Ebook
  20. T.S. Chung, Y.Z. Li," A Hybrid GA Approach for OPF with Consideration of FACTS Devices", IEEE Power Engineering Review, February 2001

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