基于随机衰减因子粒子群的最优潮流计算  

Optimal power flow calculation method based on random attenuation factor particle swarm optimization

作　　者：孙淑琴[1] 吴晨悦 颜文丽 李铭男 刘育杰 杨博华 SUN Shuqin;WU Chenyue;YAN Wenli;LI Mingnan;LIU Yujie;YANG Bohua(Lab of Geo-Exploration and the Instrumentation Ministry of Education of China,Jilin University,Changchun 130026,China)

机构地区：[1]地球信息探测仪器教育部重点实验室(吉林大学),吉林长春130026

出　　处：《电力系统保护与控制》2021年第13期43-52,共10页Power System Protection and Control

基　　金：国家电网科技项目资助“大电网预调度关键技术研究及应用”(5211UZ18006K);客户侧综合能源系统仿真分析平台关键技术研究与开发项目资助(3R219K960537)。

摘　　要：针对传统粒子群算法自身存在的易早熟收敛和在问题复杂时无法寻找到最优解等缺陷,分析了其位置和速度状态转移公式中各项的含义及各项绝对值在搜索过程中的增减变化趋势和特点,并据此改进标准粒子群算法中的转移公式和初始化过程。与已有活跃目标点改进方法对比的同时,提出了基于随机衰减因子粒子群算法。分析将其应用于电力系统最优潮流计算中的方法,包括N−1粒子维度设定方式以及将适应度函数与惩罚函数结合的思想,用以控制优化过程中可能出现的越限情况,得到较为完整的计算流程和优化模型。使用IEEE-7节点、某实际37节点、IEEE-118节点的三个网络,验证了计算方法的有效性。The traditional particle swarm algorithm has shortcomings, such as premature convergence and the inability to find the optimal solution when the problem is complex. Thus this paper analyzes the meanings of the terms in the position and velocity state transition formula and the absolute values of the terms as the search process progresses. On this basis, the transfer formula and initialization process in the standard particle swarm optimization algorithm are improved. Compared with the existing active target improvement methods, a particle swarm optimization algorithm based on a random attenuation factor is proposed. The methods used in the calculation of the optimal power flow of the power system are analyzed, including the N-1 particle dimension setting method and the thought combined fitness function with the penalty function to control the possible out-of-limit situations in the optimization process. Then a more complete calculation process and optimization model is obtained. The IEEE-7, an actual 37-node network and IEEE-118-node network are used to verify the validity of the calculation method.

关 键 词：粒子群算法 随机衰减因子 粒子维度 惩罚函数 最优潮流 

分 类 号：TM744[电气工程—电力系统及自动化]