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Hybrid GA-PSO for optimal placement of static VAR compensators in power system

机译:混合GA-PSO可在电力系统中优化放置静态无功补偿器

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In recent years, genetic algorithm (GA), particle swarm optimization (PSO) and hybrid genetic algorithm particle swarm optimization (HGAPSO) have attracted considerable attention among various modern heuristic optimization techniques. In this study the HGAPSO, PSO and GA optimization techniques are used for to search the optimal placement and sizing of static VAR compensator (SVC) in power system. The objective function is defined for reducing power loss, voltage deviation and investment costs of SVC. The effectiveness of the proposed hybrid based approach is applied and demonstrated on IEEE 30 Bus network. The results show that the proposed hybrid HGAPSO compared with PSO and GA optimization for performs and giving better solution.
机译:近年来,遗传算法(GA),粒子群优化(PSO)和混合遗传算法粒子群优化(HGAPSO)在各种现代启发式优化技术中引起了相当大的关注。在这项研究中,采用HGAPSO,PSO和GA优化技术来搜索电力系统中静态VAR补偿器(SVC)的最佳位置和尺寸。定义目标函数是为了减少SVC的功率损耗,电压偏差和投资成本。所提出的基于混合的方法的有效性已在IEEE 30总线网络上得到应用和证明。结果表明,所提出的混合HGAPSO与PSO和GA优化相比具有更好的性能和解决方案。

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