Advertisement

Simulated Annealing Based Real Power Loss Minimization Aspect for a Large Power Network

  • Syamasree Biswas (Raha)
  • Kamal Krishna Manadal
  • Niladri Chakraborty
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8297)

Abstract

Real field power systems are suffering from the various problems since two decades passed. Among them one of the vital problems is the real power loss minimization issue. In this paper the said issue is tried to be solved utilizing one of the interesting meta-heuristic technique i.e., Simulated Annealing method. While solving the same, few control and state variables are controlled and monitored such that system parametric violations do not occur. Finally obtained results are compared with other reported technique which proves the effectiveness of the approached technique.

Keywords

Real power loss minimization Reactive power dispatch Voltage Stability Simulated Annealing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abido, M.A.: Optimal power flow using tabu search algorithm. Electrical Power Components Systems 30(5), 469–483 (2002)CrossRefGoogle Scholar
  2. 2.
    Abou, E.E.A., Abido, M.A., Spea, A.R.: Optimal power flow using differential evolutionary algorithm. Electrical Power Systems Research 80, 878–885 (2010)CrossRefGoogle Scholar
  3. 3.
    Bansilal, D.T., Parthasarathy, K.: Optimal reactive power dispatch algorithm for voltage stability improvement. Electrical Power and Energy Systems 18(70), 461–468 (1996)Google Scholar
  4. 4.
    Brest, J.: Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)CrossRefGoogle Scholar
  5. 5.
    Liang, C.H., Chung, C.Y., Wong, K.P., Duan, X.Z., Tse, C.T.: Study of differential evolution for optimal reactive power flow. IET Generation Transmission Distribution 1(2), 253–260 (2007)CrossRefGoogle Scholar
  6. 6.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science New Series 220(4598), 671–680 (1983)zbMATHMathSciNetGoogle Scholar
  7. 7.
    Mahadevan, K., Kannan, P.S.: Comprehensive learning particle swarm optimization for reactive power dispatch. Applied Soft Computing 10, 641–652 (2010)CrossRefGoogle Scholar
  8. 8.
    Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equations of State Calculations by Fast Computing Machines. Journal of Chemical Physics 21(6), 1087–1092 (1953)CrossRefGoogle Scholar
  9. 9.
    Osman, M.S., Abo-Sinna, M.A., Mousa, A.A.: A solution to the optimal power flow using genetic algorithm. Applied Math Computation 155(2), 391–405 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Sadat, H.: Power system analysis, ch. 6. Tata McGraw-Hill Publishing Company limited (2008) (fourteenth reprint)Google Scholar
  11. 11.
    Subbaraj, P., Rajnarayanan, P.N.: Optimal reactive power dispatch using self-adaptive real coded genetic algorithm. Electrical Power Systems Research 79, 374–381 (2009)CrossRefGoogle Scholar
  12. 12.
    Talbi, E.G.: Metaheuristics from design to implementation, ch. 2, 3. John Wiley & Sons (2009)Google Scholar
  13. 13.
    Varadarajan, M., Swarup, K.S.: Differential evolution approach for optimal reactive power dispatch. Electrical Power and Energy System 30, 435–441 (2008)CrossRefGoogle Scholar
  14. 14.
    Wu, Q.H., Cao, Y.J., Wen, J.Y.: Optimal reactive power dispatch using an adaptive genetic algorithm. International Journal of Electrical Power Energy System 20, 563–569 (1998)CrossRefGoogle Scholar
  15. 15.
    Wu, Q.H., Ma, J.T.: Power system optimal reactive power dispatch using evolutionary programming. IEEE Transactions on Power Systems 10(3), 1243–1249 (1995)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Power system test case archive (Decembar 2006), http://www.ww.washington.edu/research/pstca
  17. 17.
    Jwo, W.S., Liub, C.W.: Large-scale optimal VAR planning by hybrid simulated annealing/genetic algorithm. Electrical Power and Energy Systems 21, 39–44 (1999)CrossRefGoogle Scholar
  18. 18.
    Roa-Sepulveda, C.A., Pavez-Lazo, B.J.: A solution to optimal power flow using simulated annealing. Electrical Power and Energy Systems 25, 47–57 (2003)CrossRefGoogle Scholar
  19. 19.
    Standard PSO 2007 (SPSO-07) on the Particle Swarm Central, Programs section (2007), http://www.particleswarm.info

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Syamasree Biswas (Raha)
    • 1
  • Kamal Krishna Manadal
    • 1
  • Niladri Chakraborty
    • 1
  1. 1.Dept. of Power EngineeringJadavpur UniversityKolkataIndia

Personalised recommendations