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投稿时间：2015-06-25
投稿时间：2015-06-25
中文摘要: 针对生物地理学算法（BBO）信息利用能力强但搜索能力不强的问题，提出了一种结合遗传算法改进变异操作的算法.改进算法充分利用了遗传算法的搜索能力，使算法的寻优能力得到了很大的改善.将该算法应用于IEEE34节点的系统，采用分区的方法进行无功补偿优化.算例表明：与基本BBO算法、遗传算法的无功优化相比，改进算法在计算速度和优化效果方面都具有明显的优势.
Abstract:In view of the fact that biogeography algorithm information utilization is strong but search is not,an improved algorithm is used to improve the operation of variation and genetic algorithm,the improved algorithm is called BBOGA algorithms.Because the improved algorithm makes full use of the genetic algorithm search capabilities,which is coupled with the ability to use the algorithm biogeography of information,so that the algorithm optimization capability can be greatly improved,it is used for reactive power compensation optimization IEEE34-node system,which is partitioned approach.The example shows that basic BBO improved algorithm,genetic algorithm compared to reactive power optimization has obvious advantages in terms of computational speed and optimization effect.
文章编号：20181016 中图分类号： 文献标志码：
基金项目:上海市电站自动化技术重点实验室项目（13DZ2273800）.
| 作者 | 单位 | |
| 王海燕 | 上海电力学院 | |
| 胡泽浩 | 国网浙江省义乌市供电公司 | 1024667108@qq.com |
| Author Name | Affiliation | |
| WANG Haiyan | Shanghai University of Electric Power, Shanghai 200090, China | |
| HU Zehao | State Grid Zhejiang Yiwu Power Supply Co., Yiwu 322000, China | 1024667108@qq.com |
引用文本：
王海燕,胡泽浩.改进生物地理学优化算法在配电网无功优化中的应用[J].上海电力大学学报,2018,34(1):85-89,94.
WANG Haiyan,HU Zehao.Biogeography Improved Optimum Algorithm in Reactive Power Optimization[J].Journal of Shanghai University of Electric Power,2018,34(1):85-89,94.
王海燕,胡泽浩.改进生物地理学优化算法在配电网无功优化中的应用[J].上海电力大学学报,2018,34(1):85-89,94.
WANG Haiyan,HU Zehao.Biogeography Improved Optimum Algorithm in Reactive Power Optimization[J].Journal of Shanghai University of Electric Power,2018,34(1):85-89,94.