| 引用本文: | 张涛,史苏怡,徐雪琴.基于二进制量子粒子群算法的含分布式电源配电网重构[J].电力系统保护与控制,2016,44(4):22-28.[点击复制] |
| ZHANG Tao,SHI Suyi,XU Xueqin.Distribution network reconfiguration with distributed generation based on improved quantum binary particle swarm optimization[J].Power System Protection and Control,2016,44(4):22-28[点击复制] |
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| 摘要: |
| 配电网重构是一个复杂的非线性组合优化问题。为了克服基本优化算法易陷入局部最优解的问题，提出了一种改进的二进制量子粒子群算法(BQPSO)，对含分布式电源(DG)的配电网重构模型进行求解。通过引入遗传算法的交叉操作和变异操作来避免早熟来提高算法的全局搜索能力，改进了算法的性能。并且选择了适当的不可行解处理方式来提高了算法的计算效率。最后通过对IEEE33节点配电系统进行仿真，验证所提算法在求解重构问题时得到的解更好，收敛速度和全局寻优能力都有提升。 |
| 关键词: 配电网重构 分布式电源 二进制量子粒子群 遗传算法 |
| DOI：10.7667/PSPC150698 |
| 投稿时间：2015-04-24修订日期：2015-11-22 |
| 基金项目:国家自然科学基金项目(51307097) |
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| Distribution network reconfiguration with distributed generation based on improved quantum binary particle swarm optimization |
| ZHANG Tao,SHI Suyi,XU Xueqin |
| (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China) |
| Abstract: |
| Reconstruction of distribution network is a complex nonlinear combinatorial optimization problem. This paper presents an improved binary quantum particle swarm optimization (BQPSO), in order to overcome the basic optimization algorithm is easy to fall into local optimal solution. The algorithm is used to solve the reconstruction of the distribution network model with distributed generation (DG). By introducing the crossover operation and mutation operation of genetic algorithm to jump out precocity to improve the global search ability of the algorithm, the performance of the algorithm is improved. And the appropriate way of infeasible solution treatment is selected to improve the computational efficiency of the algorithm. Finally, through the IEEE33 node distribution system simulation, it is verified that the algorithm proposed is better in solving the reconstruction problem, and convergence speed and global optimization ability are improved. |
| Key words: distribution network reconfiguration distributed generation (DG) binary quantum particle swarm optimization (BQPSO) genetic algorithms |
