| 基于蜜蜂进化型遗传算法的电力系统无功优化 |
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| 引用本文： | 杨晨,宗晓萍.基于蜜蜂进化型遗传算法的电力系统无功优化[J].河北大学学报(自然科学版),2013,33(2):198-203. |
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| 作者姓名： | 杨晨 宗晓萍 |
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| 作者单位： | 河北大学电子信息工程学院,河北保定,071002 |
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| 基金项目： | 国家自然科学基金资助项目 |
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| 摘 要： | 采用蜜蜂进化机制与遗传算法相结合的蜜蜂进化型遗传算法(bee evolutionary genetic algo-rithm,BEGA)对电力系统进行无功优化计算.该算法以一定概率将蜂王(最优个体)与雄蜂(被选的个体)2部分进行交叉,因此对最优个体包含信息的开采能力得以增强.随机种群的引入,降低了算法出现过早收敛的可能性,保持了种群多样性.应用BEGA对IEEE6节点系统进行无功优化计算的结果表明:较其他算法,BEGA具有更强的全局寻优能力和更快的收敛速度.
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| 关 键 词： | 电力系统 无功优化 蜜蜂进化 遗传算法 |
Bee evolutionary genetic algorithm for reactive power optimization in power systems |
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| YANG Chen
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ZONG Xiaoping.Bee evolutionary genetic algorithm for reactive power optimization in power systems[J].Journal of Hebei University (Natural Science Edition),2013,33(2):198-203. |
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| Authors: | YANG Chen ZONG Xiaoping |
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| Institution: | (College of Electronic and Information Engineering,Hebei University,Baoding 071002,China) |
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| Abstract: | A method based on bee evolution modifying genetic algorithm(BEGA)is presented for power system reactive power optimization.In this algorithm,the best chromosome called queen-bee among the current population is crossover with drones selected according to a certain crossover probability,which enhances the exploitation of searching global optimum.In order to avoid premature convergence,BEGA introduces a random population that extends search area.Consequentially it keeps the diversity of population.The presented method has been tested in IEEE6 bus systems,compared with other algorithms,the results show that: the ability of overall searching optimal solution is better and convergence speed is higher. |
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| Keywords: | electric power systems reactive power optimization bee evolutionary genetic algorithm |
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