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Ant Colony Optimization Algorithm Based Optimal Reactive Power Dispatch to Improve Voltage Stability

机译:基于蚁群优化算法的最优无功功率调度,提高电压稳定性

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To enhance the voltage stability of the power system, several traditional and Artificial Intelligence (AI) techniques have been proposed. This paper proposes a procedure using Ant Colony Optimization (ACO) Algorithm for improving voltage stability interms 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.
机译:为了提高电力系统的电压稳定性,已经提出了几种传统和人工智能(AI)技术。本文提出了一种使用蚁群优化(ACO)算法来改善系统参数增强的电压稳定性域的过程和最佳无功功率调度的过程,其目的是最小化负载总线的L折射率值的平方和。变压器抽头更换器,发电机励磁器可切换var(伏安可反应)源/静态VAR补偿器(SVC)用作控制变量,以提高系统参数,因此电力系统的电压稳定性。开发的ACO算法在IEEE等效实际南部地区印度24总线电力系统上进行了测试。提出了ACO的性能,并将仿真结果与从传统线性编程(LP)方法获得的仿真结果进行了比较,以便理解和说明目的。

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