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Multi-objective for optimal placement and sizing DG units in reducing loss of power and enhancing voltage profile using BPSO-SLFA

机译:多目标是最佳放置和尺寸DG单位减少功率损耗和使用BPSO-SLFA增强电压曲线

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Algorithms are used to optimize both single and multi-objective system limits. This research aimed to detect the optimal location and size of the DGs, which can significantly minimize power loss and improve the stability of the voltage. The research uses binary particle swarm optimization and shuffled frog leap (BPSO-SLFA) algorithms for simulation and testing of an optimal power flow (OPF) on 33 and 69 bus radial distribution system. The result shows that the algorithms give better DG allocation and minimizes the power losses but at the nascent stage of advancement. The power losses associated with the system have significantly reduced up to 31.8244kW using multi-DGs reconfiguration placement. The outcomes are established to verify the potency of the recommend algorithm to minimize losses, general improvement in voltage profiles and cost saving for various distribution system. However, the proposed methodology can be used as a reliable method in DG settings and sizing in distribution network system which produce better outputs rather than hybrid grey wolf optimization (GWO) and hybrid big bang big crunch.
机译:算法用于优化单个和多目标系统限制。该研究旨在检测DGS的最佳位置和大小,这可以显着降低功率损耗并提高电压的稳定性。该研究使用二进制粒子群优化和随机交叉的青蛙跳跃(BPSO-SLFA)算法进行仿真和测试33和69总线径向分布系统的最佳功率流量(OPF)。结果表明,该算法提供更好的DG分配,并最大限度地减少功率损耗,而是在提升的进步阶段。使用多DGS重新配置放置,与系统相关联的功率损耗显着降低至31.8244KW。建立了结果,以验证推荐算法的效力,以最大限度地减少损耗,一般提高电压谱和各种配电系统的成本节约。然而,所提出的方法可以用作DG设置中的可靠方法,并在分发网络系统中施胶,产生更好的输出而不是混合灰狼优化(GWO)和混合大爆炸大咬腹。

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