Home            Contact us            FAQs
    
      Journal Home      |      Aim & Scope     |     Author(s) Information      |      Editorial Board      |      MSP Download Statistics

     Research Journal of Applied Sciences, Engineering and Technology


Modified Particle Swarm Optimization for Solution of Reactive Power Dispatch

1Ali Nasser Hussain, 2Ali Abdulabbas Abdullah and 1Omar Muhammed Neda
1Department of Electrical Power Engineering Techniques, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq
2AL-Najaf Engineering Technical College, AL-Furat AL-Awsat Technical University, AL-Najaf, Iraq
Research Journal of Applied Sciences, Engineering and Technology  2018  8:316-327
http://dx.doi.org/10.19026/rjaset.15.5917  |  © The Author(s) 2018
Received: March 22, 2018  |  Accepted: April 23, 2018  |  Published: August 15, 2018

Abstract

Reactive Power Dispatch (RPD) is a complex, non-continuous and it is famous and essential problem in the power system. The calculation of this problem is really part of optimal load flow calculations. In this study, two types of Particle Swarm Optimization (PSO) algorithm are utilize as an optimization tools to solve RPD problem in order to minimize real Power Loss (PLoss) in the power system and keep voltage at all buses within acceptable limit. First type of PSO algorithm is Conventional PSO and the second type is utilize to improve the searching quality, also to decrease the time calculation and to enhance the convergence characteristic in the first type, it is called Modified PSO (MPSO). These types of PSO algorithm are tested on IEEE Node- 14, Node-30, Node-57 and Node-118 power systems to test their efficiency and ability in solving RPD problem in small and large power systems. The simulation results in four power systems show that the MPSO algorithm has a better performance in decreasing losses, decreasing time calculation and enhancement of voltage profile when compared to the Conventional PSO and other algorithms that reported in the literature.

Keywords:

Conventional PSO, modified PSO, optimal load flow calculations, power loss, reactive power dispatch, voltage profile,


References

  1. Jeyadevi, S., S. Baskar, C.K. Babulal and M. Willjuice Iruthayarajan, 2011. Solving multi objective optimal reactive power dispatch using modified NSGA-II. Int. J. Elec. Power, 33(2): 219-228.https://doi.org/10.1016/j.ijepes.2010.08.017
    CrossRef    
  2. Kennedy, J. and R. Eberhart, 1995. Particle swarm optimization. Proceeding of the IEEE International Conference on Neural Networks. Perth, Australia, Piscataway NJ, 4: 1942-1948.https://doi.org/10.1109/ICNN.1995.488968
    CrossRef    
  3. Lee, K.Y., Y.M. Park and J.L. Ortiz, 1985. A united approach to optimal real and reactive power dispatch. IEEE T. Power Ap. Syst., 104(5): 1147-1153.https://doi.org/10.1109/TPAS.1985.323466
    CrossRef    
  4. Yoshida, H., K. Kawata, Y. Fukuyama, S. Takayama and Y. Nakanishi, 2000. A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE T. Power Syst., 15(4): 1232-1239.
    CrossRef    
  5. Abido, M.A., 2006. Multiobjective optimal VAR dispatch using strength Pareto evolutionary algorithm. Proceeding of the 2006 IEEE Congress on Evolutionary Computation (CEC), pp: 730-736.
    CrossRef    
  6. Abou El Ela, A.A., M.A. Abido and S.R. Spea, 2011. Differential evolution algorithm for optimal reactive power dispatch. Electr. Power Syst. Res., 81(2): 458-464.
    CrossRef    
  7. Aoki, K., A. Nishikori and R.T. Yokoyama, 1987. Constrained load flow using recursive quadratic programming. IEEE T. Power Syst., 2(1): 8-16.
    CrossRef    
  8. Badar, A.Q.H., B.S. Umre and A.S. Junghare, 2012. Reactive power control using dynamic particle swarm optimization for real power loss minimization. Int. J. Elec. Power, 41: 133-136.
    CrossRef    
  9. Bakare, G.A., G.K. Venayagamoorthy and U.O. Aliyu, 2005. Reactive power and voltage control of the Nigerian grid system using micro-genetic algorithm. Proceeding of IEEE Power and Engineering Society General Meeting, pp: 1916-1922.
    CrossRef    
  10. Bhagwan Das, D. and C. Patvardhan, 2003. A new hybrid evolutionary strategy for reactive power dispatch. Electr. Power Syst. Res., 65(2): 83-90.
    CrossRef    
  11. Bhattacharya, A. and P.K. Chattopadhyay, 2010. Solution of optimal reactive power flow using biogeography-based optimization. Int. J. Electr. Electron. Eng., 4(3): 621-629.
  12. Carpentier, J., 1962. Contribution to the economic dispatch problem. Bull. Soc. Fr. Electr., 3(8): 431-447.
  13. Dai, C., W. Chen, Y. Zhu and X. Zhang, 2009. Seeker optimization algorithm for optimal reactive power dispatch. IEEE T. Power Syst., 24(3): 1218-1231.
    CrossRef    
  14. Deeb, N. and S.M. Shaidepour, 1990. Linear reactive power optimization in a large power network using the decomposition approach. IEEE T. Power Syst., 5(2): 428-435.
    CrossRef    
  15. Devaraj, D., 2007. Improved genetic algorithm for multi-objective reactive power dispatch problem. Eur. T. Electr. Power., 17(6): 569-581.
    CrossRef    
  16. Duman, S., Y. Sonmez, U. Guvenc and N. Yorukeren, 2012. Optimal reactive power dispatch using a gravitational search algorithm. IET Gener. Transm. Dis., 6(6): 563-576.
    CrossRef    
  17. Durairaj, S., D. Devaraj and P.S. Kannan, 2006. Genetic algorithm applications to optimal reactive power dispatch with voltage stability enhancement. J. Inst. Eng. (India): Electr. Eng. Div., 87: 42-47.
    Direct Link
  18. Eberhart, R.C. and Y. Shi, 2000. Comparing inertia weights and constriction factors in particle swarm optimization. Proceeding of the 2000 Congress on Evolutionary Computation, (CEC00). La Jolla, CA, USA, July 16-19, pp: 84-88.
    Direct Link
  19. Ghasemi, M., S. Ghavidel, M.M. Ghanbarian and A. Habibi, 2014. A new hybrid algorithm for optimal reactive power dispatch problem with discrete and continuous control variables. Appl. Soft Comput., 22: 126-140.
    CrossRef    
  20. Granville, S., 1994. Optimal reactive dispatch through interior point methods. IEEE T. Power Syst., 9(1): 136-146.
    CrossRef    
  21. Habiabollahzadeh, H., G.X. Luo and A. Semlyen, 1989. Hydrothermal optimal power flow based on a combined linear and nonlinear programming methodology. IEEE T. Power Syst., 4(2): 530-537.
    CrossRef    
  22. Iba, K., 1994. Reactive power optimization by genetic algorithm. IEEE T. Power Syst., 9(2): 685-692.
    CrossRef    
  23. Khazali, A.H. and M. Kalantar, 2011. Optimal reactive power dispatch based on harmony search algorithm. Int. J. Elec. Power, 33(3): 684-692.
    CrossRef    
  24. Kirschen, D.S. and H.P. Van Meeteren, 1988. MW/voltage control in a linear programming based optimal power flow. IEEE T. Power Syst., 3(2): 481-489.
    CrossRef    
  25. Liang, C.H., C.Y. Chung, K.P. Wong and X.Z. Duan, 2006. Comparison and improvement of evolutionary programming techniques for power system optimal reactive power flow. IEE P-Gener. Transm. D., 153(2): 228-236.
    CrossRef    
  26. Liang, C.H., C.Y. Chung, K.P. Wong, X.Z. Duzn and C.T. Tse, 2007. Study of differential evolution for optimal reactive power flow. IET Gener. Transm. Dis., 1(2): 253-260.
    CrossRef    
  27. Liu, W.H.E., A.D. Papalexopoulos and W.F. Tinney, 1992. Discrete shunt controls in a Newton optimal power flow. IEEE T. Power Syst., 7(4): 1509-1518.
    CrossRef    
  28. Mahadevan, K. and P.S. Kannan, 2010. Comprehensive learning particle swarm optimization for reactive power dispatch. Appl. Soft Comput., 10(2): 641-652.
    CrossRef    
  29. Mehdinejad, M., B. Mohammadi-Ivatloo, R. Dadashzadeh-Bonab and K. Zare, 2016. Solution of optimal reactive power dispatch of power systems using hybrid particle swarm optimization and imperialist competitive algorithms. Int. J. Elec. Power, 83: 104-116.
    CrossRef    
  30. Momoh, J.A. and J.Z. Zhu, 1999. Improved interior point method for OPF problems. IEEE T. Power Syst., 14(3): 1114-1120.
    CrossRef    
  31. Nanda, J., D.P. Kothari and S.C. Srivastava, 1989. New optimal power-dispatch algorithm using Fletcher's quadratic programming method. IEE Proc-C, 136(3): 153-161.
    CrossRef    
  32. Niknam, T., H.D. Mojarrad and H.Z. Meymand, 2011. A novel hybrid particle swarm optimization for economic dispatch with valve-point loading effects. Energ. Convers. Manage., 52(4): 1800-1809.
    CrossRef    
  33. Pandya, S. and R. Roy, 2015. Particle swarm optimization based optimal reactive power dispatch. Proceeding of the IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp: 1-5.
    CrossRef    
  34. Quintana, V.H. and M. Santos-Nieto, 1989. Reactive-power dispatch by successive quadratic programming. IEEE T Energ. Convers., 4(3): 425-435.
    Direct Link
  35. Rajan, A. and T. Malakar, 2015. Optimal reactive power dispatch using hybrid Nelder-Mead simplex based firefly algorithm. Int. J. Elec. Power, 66: 9-24.
    CrossRef    
  36. Ramirez, J.M., J.M. Gonzalez and T.O. Ruben, 2011. An investigation about the impact of the optimal reactive power dispatch solved by DE. Int. J. Elec. Power, 33(2): 236-244.
    CrossRef    
  37. Subbaraj, P. and P.N. Rajnarayan, 2009. Optimal reactive power dispatch using self-adaptive real coded Genetic algorithm. Electr. Power Syst. Res., 79(2): 374-381.
    CrossRef    
  38. Tehzeeb-Ul-Hassan, H., R. Zafar, S.A. Mohsin and O. Lateef, 2012. Reduction in power transmission loss using fully informed particle swarm optimization. Int. J. Elec. Power, 43(1): 364-368.
    CrossRef    
  39. Varadarajan, M. and K.S. Swarup, 2008. Differential evolution approach for optimal reactive power dispatch. Appl. Soft Comput., 8(4): 1549-1561.
    CrossRef    
  40. Vlachogiannis, J.G. and K.Y. Lee, 2006. A comparative study on particle swarm optimization for optimal steady-state performance of power systems. IEEE T. Power Syst., 21(4): 1718-1728.
    CrossRef    
  41. Wu, Q.H. and J.T. Ma, 1995. Power system optimal reactive power dispatch using evolutionary programming. IEEE T. Power Syst., 10(3): 1243-1249.
    CrossRef    
  42. Wu, Q.H., Y.J. Cao and J.Y. Wen, 1998. Optimal reactive power dispatch using an adaptive genetic algorithm. Int. J. Elec. Power, 20(8): 563-569.
    CrossRef    
  43. Yan, W., F. Liu, C.Y. Chung and K.P. Wong, 2006. A hybrid genetic algorithm-interior point method for optimal reactive power flow. IEEE Trans. Power Syst., 21(3): 1163-1209.
    CrossRef    
  44. Yan, X. and V.H. Quintana, 1999. Improving an interior-point-based OPF by dynamic adjustments of step sizes and tolerances. IEEE T. Power Syst., 14(2): 709-717.
    CrossRef    
  45. Yang, C.F., G.G. Lai, C.H. Lee, C.T. Su and G.W. Chang, 2012. Optimal setting of reactive compensation devices with an improved voltage stability index for voltage stability enhancement. Int. J. Elec. Power, 37: 50-57.
    CrossRef    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
Submit Manuscript
   Information
   Sales & Services
Home   |  Contact us   |  About us   |  Privacy Policy
Copyright © 2024. MAXWELL Scientific Publication Corp., All rights reserved