摘要
智能算法求解电力系统优化问题时,除算法的特有参数外,种群规模和迭代次数两个基本参数也会对电力系统优化效果产生较大影响。以IEEE30节点系统和IEEE118节点系统为算例,使用粒子群算法,在控制适应度值的总计算次数恒定(取为5000)的情况下,对两算例的网损和电压偏移指标进行优化求解。结果表明惯性权重取随机值较传统的线性递减方式能够取得更好的寻优效果。对于IEEE30节点系统,迭代次数取种群规模的3倍左右时优化效果较好。对于IEEE118节点系统,迭代次数取种群规模的50倍左右时优化效果较好。本研究可为同等规模算例的优化问题提供参考。
In addition to the specific parameters of each algorithm,the basic parameters such as population size and number of iterations also have great impact on the optimization effect of power system.Setting the total calculation times of fitness value to 5000,the network loss and voltage deviation of IEEE 30-bus system and IEEE 118-bus system are optimized using particle swarm optimization algorithm in this paper.The results show that the optimization effect is better when the value of inertia weight is random than the traditional linearly decreasing.For IEEE 30-bus system,the optimization effect is better when number of iterations is three times as big as population size.For IEEE 118-bus system,the optimization effect is better when number of iterations is fifty times as big as population size.This research can provide some reference for the optimization problem of the same scale power system.
作者
张凡
王雷
赵娟
吴磊
ZHANG Fan;WANG Lei;ZHAO Juan;WU Lei
出处
《青海电力》
2020年第2期12-20,共9页
Qinghai Electric Power
基金
国网公司科技项目(5200-201956111A-0-0-00)。
关键词
种群规模
迭代次数
粒子群算法
优化效果
population size
number of iterations
PSO
optimization effect
