Optimal Reactive Power Dispatch using Hybrid Grey Wolf Optimization Technique

##plugins.themes.academic_pro.article.main##

Z. B. Parekh
Bhavik N. Suthar

Abstract

In this paper, Hybrid Grey Wolf Optimization (HGWO) method is used to find the set of optimal control variables of Optimal Reactive Power Dispatch (ORPD) problem, such as generators terminal voltage, position of tap changers of transformers, and number of switchable capacitor banks. The performance and feasibility of the proposed algorithm are demonstrated through IEEE 30-bus system. Comparison of obtained results with simple GWO technique and other methods reported in the literature shows clearly the superiority of HGWO algorithm over other recently published algorithms in regards to real power transmission losses minimization hence confirmation of the efficiency of HGWO algorithm in providing optimal solution.

##plugins.themes.academic_pro.article.details##

How to Cite
Parekh, Z. B., & Suthar, B. N. (2018). Optimal Reactive Power Dispatch using Hybrid Grey Wolf Optimization Technique. Power Research - A Journal of CPRI, 178–183. https://doi.org/10.33686/pwj.v14i2.144093

References

  1. Granville S. Optimal reactive dispatch through interior point methods. IEEE Transactions Power Systems. 1994; 9(1):136–46. https://doi.org/10.1109/59.317548
  2. Momoh JA, Guo SX, Ogbuobiri EC, Adapa R. The quadratic interior point method solving power system optimization problems. IEEE Transactions Power Systems. 1994; 9(3):1327–36. https://doi.org/10.1109/59.336133
  3. Momoh JA, El-Hawary ME, Adapa R. A review of selected optimal power flow literature to 1993. II. Newton, linear programming and interior point methods. IEEE Transactions. Power Systems. 1999; 14(1):105–11. https:// doi.org/10.1109/59.744495
  4. Zhao B, Guo CX, Cao YJ. A multiagent-based particle swarm optimization approach for optimal reactive power dispatch. IEEE Transactions Power Systems. 2005 May; 20(2):1070–8. https://doi.org/10.1109/TPWRS.2005.846064
  5. Cai G, Ren Z, Yu T. Optimal reactive power dispatch based on modified particle swarm optimization considering voltage stability. 2007 IEEE Power Engineering Society General Meeting; 2007. p. 1–5. https://doi.org/10.1109/ PES.2007.386101
  6. Abbasy A, Hosseini SH. Ant colony optimization-based approach to optimal reactive power dispatch: A comparison of various ant systems. 2007 IEEE Power Engineering Society Conference and Exposition in Africa – PowerAfrica; 2007. p. 1–8. https://doi.org/10.1109/PESAFR.2007.4498067
  7. Dai C, Chen W, Zhu Y, Zhang X. Seeker optimization algorithm for optimal reactive power dispatch. IEEE Transactions Power Systems. 2009 Aug; 24(3):1218–31. https://doi.org/10.1109/TPWRS.2009.2021226
  8. Wu QH, Ma JT. Power system optimal reactive power dispatch using evolutionary programming. IEEE IEEE Transactions. Power Systems. 1995; 10(3):1243–9. https:// doi.org/10.1109/59.466531
  9. Lai LL, Ma JT. Application of evolutionary programming to reactive power planning-comparison with nonlinear programming approach. IEEE Transactions Power Systems. 1997; 12(1):198–206. https://doi.org/10.1109/59.574940
  10. Yan W, Lu S, Yu DC. A novel optimal reactive power dispatch method based on an improved hybrid evolutionary programming technique. IEEE Transactions Power Systems. 2004 May; 19(2):913–18. https://doi.org/10.1109/ TPWRS.2004.826716
  11. Wu QH, Cao YJ, Wen JY. Optimal reactive power dispatch using an adaptive genetic algorithm. International Journal of Electrical Power and Energy Systems. 1998 Nov; 20(8):563–9. https://doi.org/10.1016/S0142-0615(98)00016-7
  12. Wu QH, Cao YJ. Stochastic optimization of control parameters in genetic algorithms. Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC ‘97); 1997. p. 77–80.
  13. Chen C–R, Lee C–Y, Hsu Y-F, Chao H–W. Optimal reactive power dispatch of power systems using a modified genetic algorithm. 2004 International Conference on Power System Technology, 2004. PowerCon. 2004; 2:1266–9.
  14. Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Advances in Engineering Software. 2014; 69:46–61. https:// doi.org/10.1016/j.advengsoft.2013.12.007
  15. Roy PK, Ghoshal SP, Thakur SS. Optimal VAR control for improvements in voltage profiles and for real power loss minimization using Biogeography Based Optimization. International Journal of Electrical Power and Energy Systems. 2012 Dec; 43(1):830–8. https://doi.org/10.1016/j.ijepes.2012.05.032
  16. Vlachogiannis JG, Lee KY. A comparative study on particle swarm optimization for optimal steady-state performance of power systems. IEEE Transactions Power Systems. 2006; 21(4). https://doi.org/10.1109/TPWRS.2006.883687