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Multi-objective optimal active power dispatch using swarm optimization techniques

机译:使用群体优化技术的多目标最优有功功率分配

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This paper deals with application of swarm intelligence based algorithms to solve optimal active power dispatch problem in an efficient manner. In this paper well established artificial intelligent algorithms viz cuckoo search (CS) method; Particle swarm optimization (PSO) and Modified PSO are used to solve a multi-objective optimal power flow (OPF) problem. Some modifications in PSO are carried to enhance its efficiency and search ability. Here combined composite function of fuel cost and emission minimization has been considered. These two objectives are combined into single function of cost with the help of price factor and weight ratios. All three methods are implemented on IEEE 30-bus 6-Generator system and simulation results are compared. The composite results demonstrate the potential of Modified PSO and Cuckoo search method to solve the combined problem of economic dispatch with emission control.
机译:本文讨论了基于群体智能的算法的应用,以有效地解决最优有功功率分配问题。本文建立了完善的人工智能算法,即布谷鸟搜索(CS)方法。粒子群优化(PSO)和修正PSO用于解决多目标最优潮流(OPF)问题。对PSO进行了一些修改,以提高其效率和搜索能力。在这里,已经考虑了燃料成本和排放最小化的组合复合功能。在价格因素和权重比的帮助下,这两个目标被组合为单一成本函数。这三种方法均在IEEE 30总线6生成器系统上实现,并比较了仿真结果。综合结果表明,改进的PSO和布谷鸟搜索方法可以解决经济调度与排放控制相结合的问题。

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