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A Multi-objective Shuffled Bat algorithm for optimal placement and sizing of DGs with load variations

机译:具有负荷变化的DG的最佳放置和大小调整的多目标Shuffle Bat算法

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A new hybrid Multi-objective Shuffled Bat optimization algorithm is proposed in this paper for Distributed generations (DGs) optimal placement and sizing. Multiple objectives like system power losses, cost of DG and system voltage profiles are considered to evaluate the impact of DG placement and sizing for an optimal development of the distribution system with load variations. Furthermore, the study is demonstrated with different % loading such as 80,100 and 120% of base load condition. The proposed technique is tested in 33 bus distribution network, and compared against Non-dominated Sorting Genetic Algorithm II (NSGA-II).
机译:提出了一种新的混合多目标混和蝙蝠优化算法,用于分布式发电(DGs)的最优布置和尺寸确定。考虑了多个目标,例如系统功率损耗,DG的成本和系统电压曲线,以评估DG放置和选型的影响,以优化具有负载变化的配电系统。此外,该研究在不同的%负载(例如80,100和120%的基本负载条件)下得到了证明。该技术在33个公交配电网中进行了测试,并与非支配排序遗传算法II(NSGA-II)进行了比较。

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