基于差分粒子群算法的变电站选址定容规划  

The Optimization of Substation Locating and Sizing Based on DEPSO Algorithm

作　　者：陈浩 王健 CHEN Hao;WANG Jian(State Grid Anhui Electric Power Co.,Ltd. Ma′anshan Power Supply Company,Maanshan 243011,China)

机构地区：[1]国网安徽省电力有限公司马鞍山供电公司,安徽马鞍山243011

出　　处：《电力工程技术》2018年第3期118-122,共5页Electric Power Engineering Technology

摘　　要：针对标准粒子群算法(particle swarm optimization,PSO)易陷入局部最优,差分进化算法(differential evolution,DE)后期收敛速度慢的缺点,提出差分粒子群算法(differential particle swarm optimization,DEPSO)将二者进行混合优化,提高群体的收敛速度和全局寻优能力,并应用于配电网变电站规划。在变电站选址数学模型中结合Voronoi图来确定变电站供电范围和规划容量,继而校验变电站实际负载率,简化计算过程,提高搜索效率。通过某市城区远期规划实例验证得知该算法正确有效,可以满足城区配电网的规划要求。Aiming at the shortcomings that the traditional standard particle swarm optimization （PSO）tends to fall into the localoptimum and the differential evolution algorithm （DE）has a slow convergence rate in the later stage,a differential particleswarm optimization algorithm （DEPSO）is proposed to optimize both the convergence speed and the global Optimum ability,and applied to distribution network substation planning. Through the combination of Voronoi diagram in the mathematical modelof substation site selection to determine the substation power supply range and planning capacity,and then verify the substationactual load rate,simplify calculations and improve search efficiency. The longterm planning example of a city city verified thatthe algorithm is correct and effective,which can meet the planning requirements of urban distribution network.

关 键 词：粒子群算法 差分进化算法 差分粒子群算法 VORONOI图 变电站选址定容 

分 类 号：TM744[电气工程—电力系统及自动化]