Multi-objective optimal active power dispatch using swarm optimization techniques

G. Goyal, H. Mehta
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引用次数: 9

Abstract

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.
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基于群优化技术的多目标最优有功调度
本文研究了基于群智能的算法在有效解决最优有功调度问题中的应用。本文建立了较为完善的人工智能算法:布谷鸟搜索(CS)法;采用粒子群算法和改进粒子群算法求解多目标最优潮流问题。对粒子群算法进行了一些改进,提高了算法的效率和搜索能力。这里考虑了燃料成本和排放最小化的组合函数。借助于价格因子和权重比,将这两个目标合并为单一的成本函数。在ieee30总线6-Generator系统上实现了这三种方法,并对仿真结果进行了比较。综合结果表明,改进粒子群算法与布谷鸟搜索算法在解决经济调度与排放控制的结合问题上具有一定的潜力。
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