基于NSHCSA的FACTS设备多目标优化问题的求解

M. Balasubbareddy
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引用次数: 5

摘要

具有FACTS器件的最优潮流(OPF)在电力系统中起着至关重要的作用。本文针对包含静态同步串联补偿器(SSSC)和线间功率流控制器(IPFC)的串联FACTS装置的多目标最优潮流问题,提出了一种非支配排序混合布谷鸟搜索算法(NSHCSA),该算法考虑了FACTS装置的实际约束、运行约束和安装成本等不同的目标函数。提出了一些启发式规则来确定FACTS设备的最佳位置,以减少可能的位置数量。将布谷鸟搜索算法(CSA)与遗传算法(GA)相结合,形成混合布谷鸟搜索算法(HCSA)。利用模糊决策工具对多目标的Pareto前解进行优选。在带有FACTS设备的IEEE-30总线测试系统上验证了该方法的有效性。对结果进行了分析,并与现有方法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A solution to the multi-objective optimization problem with FACTS devices using NSHCSA including practical constraints
Optimal Power Flow (OPF) with FACTS devices place a vital role in power systems. In this paper, a proposed Non-dominated Sorting Hybrid Cuckoo Search Algorithm (NSHCSA) for multi objective optimal power flow problem with series FACTS devices namely Static Synchronous Series Compensator (SSSC) and Interline Power Flow Controller (IPFC) with different objective functions including the practical constraints, operating constraints and the installation cost of FACTS devices are considered for this analysis. Some heuristic rules are suggested for the optimal location of FACTS devices to reduce the number of possible locations. Cuckoo Search Algorithm (CSA) and Genetic Algorithm (GA) are combined to form the proposed Hybrid Cuckoo Search Algorithm (HCSA). The fuzzy decision making tool is used to select optimal Pareto front solution for multi objectives. The effectiveness of the proposed method is tested on IEEE-30 bus test system with FACTS devices. The results are analyzed and compared with existing methods.
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