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Constriction factor based particle swarm optimization for analyzing tuned reactive power dispatch

Constriction factor based particle swarm optimization for analyzing tuned reactive power dispatch
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摘要 The reactive power dispatch (RPD) problem is a very critical optimization problem of power system which minimizes the real power loss of the transmission system. While solving the said problem, generator bus voltages and transformer tap settings are kept within a stable operating limit. In connection with the RPD problem, solving reactive power is compensated by incorporating shunt capacitors. The particle swarm optimization (PSO) technique is a swarm intelligence based fast working optimization method which is chosen in this paper as an optimization tool. Additionally, the constriction factor is included with the conventional PSO technique to accelerate the convergence property of the applied optimization tool. In this paper, the RPD problem is solved in the case of the two higher bus systems, i.e., the IEEE 57-bus system and the IEEE ll8-bus system. Furthermore, the result of the present paper is compared with a few optimization technique based results to substantiate the effectiveness of the proposed study. The reactive power dispatch (RPD) problem is a very critical optimization problem of power system which minimizes the real power loss of the transmission system. While solving the said problem, generator bus voltages and transformer tap settings are kept within a stable operating limit. In connection with the RPD problem, solving reactive power is compensated by incorporating shunt capacitors. The particle swarm optimization (PSO) technique is a swarm intelligence based fast working optimization method which is chosen in this paper as an optimization tool. Additionally, the constriction factor is included with the conventional PSO technique to accelerate the convergence property of the applied optimization tool. In this paper, the RPD problem is solved in the case of the two higher bus systems, i.e., the IEEE 57-bus system and the IEEE ll8-bus system. Furthermore, the result of the present paper is compared with a few optimization technique based results to substantiate the effectiveness of the proposed study.
出处 《Frontiers in Energy》 SCIE CSCD 2013年第2期174-181,共8页 能源前沿(英文版)
关键词 real power loss minimization voltage stability constriction factor particle swarm optimization (PSO) real power loss minimization voltage stability constriction factor particle swarm optimization (PSO)
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参考文献23

  • 1Bansilal D T, Parthasarathy K. Optimal reactive power dispatch algorithm for voltage stability improvement. Electrical Power and Energy Systems, 1996, 18(70): 461-468.
  • 2Varadarajan M, Swamp K S. Differential evolution approach for optimal reactive power dispatch. Applied Soft Computing, 2008, 8 (4): 1549-1561.
  • 3Mahadevan K, Kannan P S. Comprehensive learning particle swarm optimization for reactive power dispatch. Applied Soft Computing, 2010, 10(2): 641 652.
  • 4Suhbaraj P, Rajnarayanan P N. Optimal reactive power dispatch using self-adaptive real coded genetic algorithm. Electric Power Systems Research, 2009, 79(2): 374-381.
  • 5Talbi E-G. Metaheuristics: From Design to Implementation. Hoboken: John Wiley & Sons, 2009.
  • 6Wu Q H, Ma J T. Power system optimal reactive power dispatch using evolutionary programming. IEEE Transactions on Power Systems, 1995, 10(3): 1243-1249.
  • 7Abido M A. Optimal power flow using tabu search algorithm. Electrical Power Components Systems, 2002, 30(5): 469-483.
  • 8Osman M S, Abo-Sinna M A, Mousa A A. A solution to the optimal power flow using genetic algorithm. Applied Mathematics and Computation, 2004, 155(2): 391-405.
  • 9Abou E1 Ela A A, Abido M A, Spea A R. Optimal power flow using differential evolutionary algorithm. Electric Power Systems Research. 2010. 80(7): 878-885.
  • 10Liang 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, 2007, 1(2): 253-260.

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