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Multi-objective optimal active power dispatch using swarm optimization techniques

机译:使用群优化技术进行多目标最佳有效功率调度

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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:本文涉及基于群体智能的算法,以有效的方式解决最佳的主动功率调度问题。本文建立了人工智能算法的良好,杜鹃搜索(CS)方法;粒子群优化(PSO)和改进的PSO用于解决多目标最佳功率流(OPF)问题。 PSO中的一些修改是为了提高其效率和搜索能力。这里考虑了燃料成本和发射最小化的组合复合功能。在价格因数和体重比率的帮助下,这两个目标被组合成单一的成本函数。所有三种方法都在IEEE 30-Bus 6发生器系统上实现,并比较仿真结果。复合结果证明了改进的PSO和杜鹃搜索方法的潜力,以解决对排放控制的经济派遣的综合问题。

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