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基于改进遗传算法的电力无线专网4G、5G基站布点规划优化方法
引用本文:张文栋,刘周峰,谢宏福,王 茗,孙玉杰,沈晓风,段接迎,周 霞.基于改进遗传算法的电力无线专网4G、5G基站布点规划优化方法[J].电力需求侧管理,2024,26(1):67-72.
作者姓名:张文栋  刘周峰  谢宏福  王 茗  孙玉杰  沈晓风  段接迎  周 霞
作者单位:国网山东省电力公司,济南 250000;国电南瑞科技股份有限公司,南京 211000;南京邮电大学 先进技术研究院,南京 210023
基金项目:国家自然基金资助重点项目(61933005)
摘    要:在电力4G无线专网中引入5G,进行4G、5G融合组网,对提高电力业务通信效率具有重要意义。首先阐述了5G在电力系统中的应用及4G、5G组网方式。然后,综合考虑电力自有业务、电力业务通信需求、电力基站建设成本、基站运行成本,构建了电力无线专网4G、5G基站布点优化模型,对4G、5G基站进行优化布点。其次对遗传算法中编码、初始化种群操作进行优化设计和改进,大幅减少算法迭代次数,提高收敛速度,降低了模型的求解复杂度。最后基于改进模型对电力4G、5G基站布点优化模型进行求解,可以快速给出4G、5G基站优化方案,满足电力通信覆盖率和经济性等要求。仿真算例验证所提模型、改进算法的合理性和有效性。

关 键 词:电力无线专网  5G通信  基站选址  遗传算法
收稿时间:2023/8/20 0:00:00
修稿时间:2023/10/4 0:00:00

Optimization method for 4G and 5G base station layout planning for electric power wireless private network based on improved genetic algorithm
ZHANG Wendong,LIU Zhoufeng,XIE Hongfu,WANG Ming,SUN Yujie,SHEN Xiaofeng,DUAN Jieying,ZHOU Xia.Optimization method for 4G and 5G base station layout planning for electric power wireless private network based on improved genetic algorithm[J].Power Demand Side Management,2024,26(1):67-72.
Authors:ZHANG Wendong  LIU Zhoufeng  XIE Hongfu  WANG Ming  SUN Yujie  SHEN Xiaofeng  DUAN Jieying  ZHOU Xia
Affiliation:State Grid Shandong Electric Power Co., Ltd., Jinan 250000;NARI Technology Development Co., Ltd.,Nanjing 211000, China;nstitute of Advanced Technology, Nanjing University of Posts andTelecommunications, Nanjing 210023, China
Abstract:The introduction of 5G into the electric power 4G wireless private network and the integration of 4 great significance for improving the efficiency of electric power business communication. Firstly application of G and 5G networks are of 5G in the power system and 4G and 5G networking methods is descirbed. Then, comprehensively considering the power own business, power business communication requirements, power base station construction costs, and base station operating costs, the power wireless private network 4G and 5G base station layout optimization models are constructed, and then optimize the layout of 4G and 5G base stations. Secondly, optimize the design and improvement of the coding and initialization population operations in the genetic algorithm, which greatly reduces the number of algorithm iterations, improves the convergence speed, and reduces the complexity of the model. Finally, the improved model solves the optimization model of power 4G and 5G base station layout. It can quickly give 4G and 5G base station optimization solutions to meet the requirements of power communication coverage and economy. Rationality and effectiveness of the proposed model and improved algorithm are verified by simulation examples.
Keywords:
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