Ant colony optimization algorithm based optimal reactive power dispatch to improve voltage stability

K. Rayudu, G. Yesuratnam, A. Jayalaxmi
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引用次数: 12

Abstract

To enhance the voltage stability of the power system, several traditional and Artificial Intelligence (Al) techniques have been proposed. This paper proposes a procedure using Ant Colony Optimization (ACO) Algorithm for improving voltage stability in terms of system parameters enhancement and optimal reactive power dispatch with the objective of minimization of the sum of the squares of the L-index values of the load buses. The transformers tap changers, Generator exciters, switchable VAR (Volt Ampere Reactive) sources / Static VAR Compensators (SVC) are used as control variables for enhancement of system parameters and hence voltage stability of power system. The developed ACO Algorithm is tested on an IEEE Equivalent practical southern-region Indian 24-bus power system. The performance of ACO is presented and simulation results are compared with those obtained from conventional Linear Programming (LP) method for understanding and illustration purpose.
基于蚁群优化算法的无功优化调度提高电压稳定性
为了提高电力系统的电压稳定性,人们提出了几种传统技术和人工智能技术。本文提出了一种以负荷母线l指标值平方和最小为目标,利用蚁群优化算法从系统参数增强和无功优化两方面提高电压稳定性的方法。变压器分接开关、发电机励磁器、可切换VAR(伏安无功)源/静态VAR补偿器(SVC)作为控制变量,增强系统参数,从而提高电力系统的电压稳定性。本文提出的蚁群算法在印度南部24母线电力系统上进行了测试。介绍了蚁群算法的性能,并将仿真结果与传统线性规划(LP)方法的仿真结果进行了比较,以便更好地理解和说明蚁群算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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