基于改进蜜蜂进化型遗传算法的含分布式电源的配电网重构
为改善分布式电源(Distributed Generation,DG)并入电网后配电网重构算法的性能,提出一种基于佳点集的蜜蜂进化型遗传算法(Bee Evolutionary Genetic Algorithm Based on Good Point Set, GBEGA)。该算法的关键有三点:1.提出一种基于佳点集的种群初始化方法,该方法比随机方法产生的种群在搜索空间更为均匀;2.引进佳点集交叉算子,该算子能在父代附近进行更加精细的搜索;3.采用自适应的交叉变异概率,有利于算法开采与勘探的平衡。将DG处理为PQ、PV两种模型,并将GBEGA与相关文献中的算法关于IEEE33和IEEE69节点...
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Published in | 电力系统保护与控制 Vol. 40; no. 15; pp. 52 - 56 |
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Main Author | |
Format | Journal Article |
Language | Chinese |
Published |
西安建筑科技大学信息与控制工程学院,陕西西安,710055%西安理工大学计算机科学与工程学院,陕西西安,710048
2012
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Subjects | |
Online Access | Get full text |
ISSN | 1674-3415 |
DOI | 10.3969/j.issn.1674-3415.2012.15.010 |
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Summary: | 为改善分布式电源(Distributed Generation,DG)并入电网后配电网重构算法的性能,提出一种基于佳点集的蜜蜂进化型遗传算法(Bee Evolutionary Genetic Algorithm Based on Good Point Set, GBEGA)。该算法的关键有三点:1.提出一种基于佳点集的种群初始化方法,该方法比随机方法产生的种群在搜索空间更为均匀;2.引进佳点集交叉算子,该算子能在父代附近进行更加精细的搜索;3.采用自适应的交叉变异概率,有利于算法开采与勘探的平衡。将DG处理为PQ、PV两种模型,并将GBEGA与相关文献中的算法关于IEEE33和IEEE69节点系统进行了对比测试。仿真结果表明,GBEGA适合于含DG的配电网重构,在全局寻优能力和收敛速度上表现出色。 |
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Bibliography: | WANG Chao-xue, Lu Zhi-qi, DONG Hui, CUI Du-wu, SUN You-tian (1. School of Information and Control Engineering, Xi'an University of Architecture & Technology, Xi'an 710055, China; 2. School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China) To improve the performance of distribution network reconstruction algorithm with distributed generation (DG), a new bee evolutionary genetic algorithm based on good point set (GBEGA) is proposed. The keys to GBEGA lies in three points as follows: firstly, proposing a population initialization method based on good point set, by which the distributing of initial population is more even in search space; secondly, introducing a crossover operator based on good point set, which has more elaborate search ability in the neighborhood of parent individuals; thirdly, adopting the self-adaption of crossover and mutation probability, which is conducive to balancing the exploration and exploitation capabilities of algorithm. Distributed generation i |
ISSN: | 1674-3415 |
DOI: | 10.3969/j.issn.1674-3415.2012.15.010 |