基于粒子群算法的IPFC最优潮流多目标优化

J. Praveen, B. Rao
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引用次数: 8

摘要

与单目标优化不同,多目标优化的主要优点是利用Pareto前沿选择控制变量。本文考虑的目标是发电成本、输电损耗和l指数。采用加权求和法求解有和无事实情况下的多目标优化最优潮流。而采用粒子群算法对目标值进行优化。本文采用的FACTS器件是基于VSC的多类型FACTS器件——线间功率流控制器(IPFC)。将该器件置于IEEE 30总线测试系统中,并对二维多目标优化的结果进行了比较。
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Multi objective optimization for optimal power flow with IPFC using PSO
Unlike Single objective optimization Multi objective optimization has the major advantage of selecting the control variables with the help of Pareto front. The objectives considered in this paper are cost of generation, transmission losses and L-index. Weighted summation method is taken as the technique for Multi objective Optimization Optimal Power Flow (MOOPF) with and without FACTS. Whereas Particle Swarm Optimization (PSO) is used for optimizing the objective values. The FACTS device used in this paper is Interline power flow controller (IPFC) which is a VSC based multi type FACTS device. The device is placed in IEEE 30-bus test system and the results are compared for two dimensional multi objective optimization optimal power flow with and without FACTS.
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