基于人工智能技术的SVC最优位置识别

R. Sheeba, M. Jayaraju, Muhammed Mansoor.O, T. N. Shanavas, K. Sundareswaran
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引用次数: 3

摘要

本文采用随机搜索技术粒子群优化(PSO)确定了IEEE 14总线系统中两个静态无功补偿器(SVCs)的最优位置。静态无功补偿器(SVC)是电网中应用最广泛的并联事实器件,因为它的成本更低,系统增强效果显著。SVC出现于二十多年前,主要用于电压支撑,安装在适当的位置可以减少功率损失。本文将该问题视为一个优化任务,并利用该新技术确定了SVC的最优位置。通过大量的计算机仿真验证了新算法的有效性,并与基于遗传算法的方法进行了比较。性能分析是通过广泛的模拟完成的,并表明所提出的分配与现有技术相当。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of optimal location of SVC through artificial intelligence techniques
In this paper, a stochastic search technique, namely Particle Swarm Optimization (PSO) is used to determine the optimal locations of two Static Var Compensators (SVCs) in an IEEE 14-bus system. Static Var compensators (SVC) are the most widely used shunt FACTS devices within power networks because of their smaller costs and significant system enhancements. Appeared about two decades ago, the SVC is mainly installed for voltage support and furthermore, when installed in a proper location, it can reduce the power loss. Here the problem is framed as an optimization task and the optimal locations of SVC are identified using the novel technique. The efficacy of the new algorithm is tested with extensive computer simulations and further compared with Genetic Algorithm (GA) based approach. The performance analysis is done through extensive simulations and shows that the proposed dispensation is on a par with existing techniques.
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