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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >Modified shuffled frog leaping algorithm for multi-objective optimal power flow with FACTS devices
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Modified shuffled frog leaping algorithm for multi-objective optimal power flow with FACTS devices

机译:FACTS装置用于多目标最优潮流的改进的改组蛙跳算法

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This paper presents a novel approach to depict Flexible AC Transmission Systems (FACTS) devices effects in power system using multi-objective optimization function. The FACTS devices can play very important roles in power system such as improve power system security, reduce generation cost, decrease transmission loss and improve the voltage stability index. Two more common FACTS devices are the Thyristor Controlled Series Capacitor (TCSC) and Static VAR Compensator (SVC) which can smoothly and rapidly change their apparent reactance and injection power respectively according to the system requirements. Determining the FACTS devices parameters in power system is too complicate and has a lot of local optima in its search space. In order to overcome above problems a new method, based on SFLA algorithm combined with a new mutation is proposed to increase the efficiency of the SFLA algorithm. Since the proposed problem is a multi-objective problem it is usual to obtain a set of solution instead one solution therefore Pareto method that uses concept of non-dominate solutions is applied to find best compromise solutions. An external repository is considered for saving all non-dominated solution, and also they are sorted by fuzzy set rule to obtain best solutions. For more validation the simulation results are compared with those in other literatures.
机译:本文提出了一种使用多目标优化函数描述电力系统中柔性交流输电系统(FACTS)设备影响的新颖方法。 FACTS器件可以在电力系统中发挥非常重要的作用,例如提高电力系统的安全性,降低发电成本,减少传输损耗并提高电压稳定性指标。晶闸管控制串联电容器(TCSC)和静态无功补偿器(SVC)是另外两种常见的FACTS器件,它们可以根据系统要求分别平滑,快速地改变其表观电抗和注入功率。确定电力系统中FACTS设备的参数过于复杂,并且在其搜索空间中具有很多局部最优值。为了克服上述问题,提出了一种基于SFLA算法结合新变异的新方法,以提高SFLA算法的效率。由于所提出的问题是一个多目标问题,通常会获得一组解决方案而不是一个解决方案,因此,采用了非支配解决方案概念的帕累托方法可用于寻找最佳折衷解决方案。考虑使用外部存储库来保存所有非支配的解决方案,并且通过模糊集规则对它们进行排序以获得最佳解决方案。为了进行更多的验证,将仿真结果与其他文献进行了比较。

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