基于粒子群算法的超前SVC控制器定位与参数设置,并与遗传算法进行了比较

D. Mondal, A. Chakrabarti, A. Sengupta
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引用次数: 15

摘要

本文旨在利用粒子群算法和遗传算法对静态无功补偿器(SVC)控制器的最优位置和整定参数进行选择,以减轻多机电力系统中的小信号振荡。虽然电力系统稳定器(pss)是解决这一问题的首选,但其性能会受到网络配置变化、负荷变化等因素的影响。因此,这里建议安装FACTS装置,SVC以获得可观的振荡阻尼。然而,任何FACTS器件的性能在很大程度上取决于其参数和在电网中的合适位置。本文采用粒子群算法和遗传算法对这一问题进行了研究。并将基于粒子群的SVC控制器与基于遗传算法的SVC控制器的性能进行了比较。在一个具有两种常见干扰的多机系统中,对所提方法的有效性进行了仿真。研究表明,即使在临界负载条件下,基于粒子群的SVC控制器也比基于遗传算法的控制器更有效。
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PSO based location and parameter setting of advance SVC controller with comparison to GA in mitigating small signal oscillations
This paper aims to select the optimal location and setting parameters of Static VAR Compensator (SVC) controller using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to mitigate small signal oscillations in a multimachine power system. Though Power System Stabilizers (PSSs) are prime choice in this issue, its performance gets affected by changes in network configurations, load variations etc. Hence installation of FACTS device, SVC has been suggested here in order to achieve appreciable damping of oscillations. However, performance of any FACTS devices highly depends upon its parameters and suitable location in the power network. In this paper PSO as well as GA based techniques are used to investigate this problem. An attempt has also been made to compare the performance of the PSO based SVC controller with its GA based design. The validity of the proposed techniques is simulated in a multimachine system following two common disturbances. It has been revealed that the PSO based SVC controller is more effective than GA based controller even during critical loading condition.
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