基于改进多组织粒子群体优化算法的配电网络变电站选址定容

提出了一种改进的多组织粒子群优化算法（RMPSO）来解决配电网络变电站选址定容问题。RMPSO中,粒子除受个体极值点和全局极值点影响外,还受组织极值点的影响。在寻优过程中,以适应度方差大小衡量粒子群体的"聚集"情况,对发生"聚集"的组织对应的组织极值点赋予变异操作,用以克服粒子群优化算法(PSO)的早熟现象。在该文提出的站址选择计算模型中,不仅考虑线路投资和网络运行费用对站址选择的影响,而且还考虑了地理信息对建站投资费用的影响,在模型上体现了变电站选址定容是地理信息和电气信息两者共同作用的结果。通过某开发新区规划实例验证了该文所提模型和方法正确性和有效性,其规划结果科学、可行。

Optimal Planning of Substation Locating and Sizing Based on Refined Multi-team PSO Algorithm

A new intelligent algorithm, refined multi-team particle swarm optimization algorithm (RMPSO), is presented to handle optimal substation locating and sizing problem. In RMPSO, particles are affected not only by local and global optima, but also by team's optima. In evolution process, the fitness variances is applied for monitoring overlapping condition of the swarms and impose the team optima mutation to avoid premature phenomena. The model of the problem involving both the substation construction investment and the geographic information makes the given solution more practical in distribution planning process. The proposed RMPSO is tested by a realistic planning project to verify the effectiveness and feasibility.