中国电力 ›› 2016, Vol. 49 ›› Issue (12): 53-57.DOI: 10.11930/j.issn.1004-9649.2016.12.053.05

• 电网 • 上一篇    下一篇

基于遗传算法的区域配电网单线图的自动布局算法

葛成余1,周博曦2,朱颂怡3,张俊玲2,孙联喜2,王金亮2,张浩2,刘书阁2,韩先鹤1   

  1. 1. 国电南瑞科技股份有限公司,江苏 南京 211106;
    2. 国网技术学院,山东 济南 250000;
    3. 南京中德保护控制系统公司,江苏 南京 211106
  • 收稿日期:2016-02-08 修回日期:2016-12-29 出版日期:2016-12-20 发布日期:2016-12-29
  • 作者简介:葛成余(1971—),男,江苏海安人,高级工程师,从事配电自动化及电能质量。

Single Line Diagram Layout and Optimization Method for Regional Distribution System Based on Genetic Algorithm

GE Chengyu1, ZHOU Boxi2, ZHU Songyi3, ZHANG Junling2, SUN Lianxi2, WANG Jinliang2, ZHANG Hao2,LIU Shuge2, HAN Xianhe1   

  1. 1. NARI Technology Co., Ltd. Nanjing 211106, China;
    2. State Grid of China Technology College, Jinan 250000, China;
    3. ZhongDe Protect and Control Company, Nanjing 211106, China
  • Received:2016-02-08 Revised:2016-12-29 Online:2016-12-20 Published:2016-12-29

摘要: 提出了一种基于遗传算法的区域配电系统单线图自动布局算法,以线路节点数目与路径差异度最小化为目标函数,引入厂站相对地理信息约束,并设计了交叉、变异算子,最后经遗传算法对节点布局进行寻优;在布线过程中,通过网格状态的数值化,采用线路路径试探、交叉重叠判定、调整的布局策略进行节点之间布线。仿真结果证明算法能使节点在图中均匀分布,走线美观清晰,计算效率和图形效果都能满足工程实用要求。

关键词: 单线图, 配电网络, 遗传算法, 交叉算子, CIM, SVG

Abstract: A GA-based single line diagram layout and optimization method for regional distribution system is proposed in this paper. The proposed method takes the minimum difference between line nodes and path as the objective function, in which the relative geographic information of substations is adopted as the constraint. With well designed crossover operator and mutation operator, the GA algorithm can be utilized to seek the optimum of nodes layout positions. While drawing the connection of line, a series of strategies are used, which include line path exploration, cross and overlap region identification and layout adjustment. The simulation results show that the algorithm allows uniform distribution of nodes in the figure. The connection line is clean and clear, and the computational efficiency and graphic effects can satisfy practical project requirements.

Key words: single line diagram, distribution network, genetic algorithm, crossover operator, CIM, SVG

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