In this manuscript, the optimal location and capacity of Unified Power Flow Controller (UPFC) based on chaotic krill herd (CKH) with runner root algorithm (RRA) for dynamic stability improvement in power system is proposed. The proposed technique is the combined execution of CKH and RRA hence it is known as CKHRA technique. Here, CKH is utilized to optimal location of unified power flow controller when occurring generator fault. CKH chooses maximal power loss line as optimal location to keep UPFC according to the objective function. The minimal voltage deviation is improved using runner root algorithm from the unified power flow controller control parameters. The minimal voltage deviation is utilized to determine the optimal unified power flow controller capacity. The CKHRA technique is executed at MATLAB/Simulink and the performance is assessed by comparison with other techniques. The performance of CKHRA technique is linked with IEEE 14 bus, IEEE 30 bus, IEEE 57 bus bench mark system, whereas the efficiency is examined against various generator fault conditions. By then the statistical analysis, result accuracy percentage and loss sensitivity index are analyzed. The comparative results thus proven the greatness of CKHRA approach and corroborate its ability to improve the dynamic stability of power system.

中文翻译：

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基于混沌磷虾群混合流道根算法的统一潮流控制器的最优位置和容量，以提高电力系统的动态稳定性

本文提出了基于混沌磷虾群（CKH）和流道根算法（RRA）的统一潮流控制器（UPFC）的最优位置和容量，以提高电力系统的动态稳定性。提出的技术是CKH和RRA的组合执行，因此被称为CKHRA技术。在此，当发生发电机故障时，CKH用于优化统一潮流控制器的位置。CKH根据目标函数选择最大功率损耗线作为最佳位置，以保持UPFC。使用流道根算法从统一的潮流控制器控制参数中改善最小电压偏差。利用最小的电压偏差来确定最佳的统一潮流控制器容量。CKHRA技术在MATLAB / Simulink上执行，并且通过与其他技术进行比较来评估性能。CKHRA技术的性能与IEEE 14总线，IEEE 30总线，IEEE 57总线基准测试系统链接在一起，而效率是针对各种发电机故障条件进行检查的。然后进行统计分析，分析结果准确性百分比和损失敏感性指标。因此，比较结果证明了CKHRA方法的优越性，并证实了其提高电力系统动态稳定性的能力。分析结果准确性百分比和损失敏感性指标。因此，比较结果证明了CKHRA方法的优越性，并证实了其提高电力系统动态稳定性的能力。分析结果准确性百分比和损失敏感性指标。因此，比较结果证明了CKHRA方法的优越性，并证实了其提高电力系统动态稳定性的能力。