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A multi-objective hybrid algorithm for feeder reconfiguration and planning of electrical distribution system

机译:一种多目标混合算法,用于配电系统的馈线重新配置和规划

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In this paper, a multi-objective Gravitational Search Algorithm (GSA) and Tabu search heuristic for feeder reconfiguration and planning of an electrical distribution system are proposed. In this strategy, the GSA has reduced the power losses and voltage deviations using relevant constraints. The optimal sizing of a distributed generator (DG) includes the best location with reduced electrical losses. The Gravitational Search Algorithm (GSA) hastens convergence with integration of a Tabu search heuristic. Then, the proposed multi-objective hybrid algorithm for planning an electrical distribution system is implemented on a MATLAB/Simulink platform. Its effectiveness is scrutinized by contrasting the results of the method under study with those of existing techniques such as ALO, LSA, CALMS and BBO-PSO. This comparison reveals the superiority of the proposed approach and affirms its potential to reduce power losses and voltage deviations.
机译:在本文中,提出了一种用于进料器重新配置的多目标重力搜索算法(GSA)和禁忌搜索启发式和指导配电系统的规划。在该策略中,GSA使用相关约束降低了功率损耗和电压偏差。分布式发电机(DG)的最佳尺寸包括具有降低电损耗的最佳位置。引力搜索算法(GSA)通过集成禁忌搜索启发式致致融合。然后,在Matlab / Simulink平台上实现了用于规划配电系统的所提出的多目标混合算法。通过对诸如ALO,LSA,CALMS和BBO-PSO的现有技术的方法对比进行审查其有效性。这种比较揭示了所提出的方法的优越性,并确认其降低功率损耗和电压偏差的可能性。

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