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ANT COLONY OPTIMIZATION FOR ACTIVE/REACTIVE OPERATIONAL PLANNING

机译:蚁群优化用于主动/无功运营计划

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This paper proposes the application of Ant Colony Optimization (ACO) for active/reactive operational planning of power systems. The ACO is a newly developed method belonging to the class of evolutionary computation methods inspired from real ants life. Specifically, ACO algorithm aims to determine the optimal settings of control variables, such as generator outputs, generator voltages, transformer taps and shunt VAR compensation devices, considered as nodes of an Ant-System (AS) graph. Results are compared to those given by Simulated Annealing for the IEEE 30-bus test system, exhibiting superior performance.
机译:本文提出了蚁群优化(ACO)在电力系统的主动/无能运营规划中的应用。 ACO是一种新开发的方法,属于来自真实蚂蚁生活的进化计算方法。具体而言,ACO算法旨在确定控制变量的最佳设置,例如发电机输出,发电机电压,变压器抽头和分流器var补偿装置,被认为是蚂蚁系统(AS)图的节点。将结果与由IEEE 30-Bus测试系统的模拟退火给出的结果进行比较,表现出卓越的性能。

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