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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >A Multi‑objective Hybrid Algorithm for Optimal Planning of Distributed Generation
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A Multi‑objective Hybrid Algorithm for Optimal Planning of Distributed Generation

机译：分布式发电最优规划的多目标混合算法

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摘要

Distributed generations (DGs) have been constantly incorporating into the distribution systems. The optimal planning (sizingand sitting) of the DGs by applying harmony search (HS)-based hybrid genetic algorithm integrated adaptive particleswarm optimization (GA-APSO) technique is exhibited in this work. The fitness evolution function has investigated as themulti-objective function (FMO), which depends on the five significant indexes such as active power loss, reactive power loss,total cost generation index, voltage deviation, and load balancing index. The ideal solution has attained by minimizing themulti-objective (MO) fitness function by deploying HS-based GA-PSO strategy. The projected work will be implementedon IEEE standard 33- and 69-bus radial distribution networks. The performance of the suggested algorithm has analyzeddependent on the five measures, such as active and reactive power loss, voltage profile improvement, reduction in voltagedeviation, and cost of operation of generation. The legitimacy of the assessed outcomes has affirmed by comparing withsome of the notable optimization algorithms.
机译：分布式世代（DG）一直不断地纳入到分布式系统中。这项工作展示了通过应用基于和谐搜索（HS）的混合遗传算法集成自适应粒子温暖优化（GA-APSO）技术对DG进行的最佳规划（大小确定和布置）。适应度演化函数已作为多目标函数（FMO）进行了研究，它取决于五个重要指标，例如有功功率损耗，无功功率损耗，总成本产生指标，电压偏差和负载平衡指标。通过部署基于HS的GA-PSO策略来最小化多目标（MO）适应度功能，从而获得了理想的解决方案。计划的工作将在IEEE标准的33总线和69总线径向分配网络上实施。所建议算法的性能已根据五种措施进行了分析，例如有功和无功功率损失，电压分布改善，电压偏差降低以及发电运营成本。通过与一些著名的优化算法进行比较，确定了评估结果的合法性。

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