| 摘要: |
| 随着用电设备使用的增多，必需新建变电站来扩大电力负荷输出规模，针对如何快速并有效地确定候选变电站最优站址的问题，提出了一种新型改进磷虾—粒子群优化算法；在算法中，首先将随机产生的初始种群分为两个子种群，分别用于磷虾算法和粒子群算法，然后再将更新后的种群合并，通过种群的分离与合并，所有个体可以彼此交换位置信息，既能增加种群多样性又可避免陷入局部解，并且在保证找到全局最优解的情况下不添加任何附加操作；为了验证算法的有效性，将其用于变电站选址的工程问题中，由仿真结果可知：混合磷虾—粒子群优化算法寻优效率高，结果准确。 |
| 关键词: 磷虾群 粒子群 变电站选址 |
| DOI： |
| 分类号: |
| 基金项目: |
|
| The Application of Improved KH Algorithm in Substation Site Selection |
|
LI Ling chun，GAO Lai xin
|
| Abstract: |
| With the increasing use of electrical equipment, it is necessary to build new substation to expand the scale of power system. Combining the advantages of KH algorithm and PSO algorithm, a novel hybrid algorithm, called KH–PSO, is presented for the application of substation site selection. In the new improved KH–PSO, the randomly generated initial population is divided into two sub populations for the krill algorithm and the particle swarm algorithm. By population separation and merging, all individuals can exchange position information with each other. This method can avoid the premature convergence, and find the global optimal solution without introducing additional operators to the basic KH and PSO algorithms. To verify its performance, experiments are carried out on the problem of substation site selection. Based on the results, we can easily infer that the hybrid KH–QPSO is more efficient and accurate. |
| Key words: KH PSO substation site selection |
