吉林大学学报(信息科学版) ›› 2022, Vol. 40 ›› Issue (3): 400-407.

• • 上一篇    下一篇

优化的 GA 算法在大型配电网络重构中的应用

姜建国, 郭晓丽, 陈 鹏, 佟麟阁, 万成德   

  1. 东北石油大学 电气信息工程学院, 黑龙江 大庆 163318
  • 收稿日期:2021-11-09 出版日期:2022-07-14 发布日期:2022-07-14
  • 作者简介:姜建国(1966— ), 男, 新疆奇台人, 东北石油大学教授, 硕士生导师, 主要从事智能电网及电气自动化、 深度学习负荷 预测研究, (Tel)86-13734583588(E-mail)jjgnepu@ 163. com。

Application of Optimized GA Algorithm in Large Distribution Network Reconfiguration

JIANG Jianguo, GUO Xiaoli, CHEN Peng, TONG Linge, WAN Chengde    

  1. School of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163318, China
  • Received:2021-11-09 Online:2022-07-14 Published:2022-07-14

摘要: 当前对配电网重构的研究多数针对于中小型网络, 而对大型网络的研究较少, 为此, 提出了一种优化的GA(Genetic Algorithm)算法应用于大型配电网络重构中, 对其基因操作过程进行优化。引入精英策略, 最大程度保留父代最优个体的遗传信息, 在变异的过程中再次引入局部变异冶, 产生的子代种群更具多样性。 对重构的目标函数引入关联系数, 简化计算复杂度。 将该算法应用于 IEEE118节点算例, 重构后线损及电压降落程度与蒙特卡洛模拟法所得结果相对比, 实验结果表明, 该算法明显更优, 并证明了该算法在大型配电网重构的可行性和有效性。

关键词: 大型配电网, 电网重构, 遗传算法, 局部变异 

Abstract: Because most of the current research on distribution network reconfiguration is aimed at small and medium-sized networks, and there is less research on large networks, an optimized GA( Genetic Algorithm) algorithm is proposed. In the large-scale distribution network reconfiguration, the gene operation process is optimized, and the elite strategy is introduced to retain the genetic information of the parent optimal individual to the greatest extent. In the process of variation, the “ local variation冶 is introduced again, and the offspring population is more diverse. The correlation coefficient is introduced into the reconstructed objective function to simplify the computational complexity. The algorithm is applied to IEEE118 node system. Compared to the results obtained by MC(Monte Carlo) simulation method, the reconstructed network loss and voltage deviation are obviously better, which proves the feasibility and effectiveness of the algorithm in large-scale distribution network reconstruction.

Key words: large distribution network, power grid reconstruction, genetic algorithm (GA), local mutation

中图分类号: 

  • TP17