基于遗传算法的电力系统VAR优化调度研究  

Research on VAR Optimal Dispatching of Power System Based on Genetic Algorithm

作　　者：张博 李校骁 胡文涛 ZHANG Bo;LI Xiaoxiao;HU Wentao(Nanjing Ningzhong Human Resources Consulting Service Co.,Ltd.,Nanjing 210000,China;Jiangsu Electric Power Information Technology Co.,Ltd.,Nanjing 210000,China)

机构地区：[1]南京宁众人力资源咨询服务有限公司,江苏南京210000 [2]江苏电力信息技术有限公司,江苏南京210000

出　　处：《微型电脑应用》2023年第3期124-126,131,共4页Microcomputer Applications

摘　　要：电力系统无功功率(VAR)优化对电力系统的经济运行具有重要意义,文章在分析不同初始种群生成方法对GA性能的影响后,改进GA求解电力系统VAR优化调度问题。在VAR优化调度模型的基础上,构建了基于GA的VAR优化算法。利用GA对决策变量进行实数编码,使用二进制竞标赛策略选择子代个体,结合模拟二进制交叉(SBX)和多项式变异方案作为交叉算子和变异算子。评估了带借位减法指令(SWB)、Niderreiter准随机抽样(NQR)、非对齐系统(NAS)和高斯采样(GS)4种初始种群生成方法。通过对IEEE-30总线系统进行实验,结果表明:采用均匀抽样的SWB伪随机发生器生成初始种群的GA,在电力系统VAR优化调度计算得到的有功功率损耗最低,仅为4.00%。Power system reactive power(VAR)optimization is of great significance to the economic operation of power systems.This article aims to consider the impact of different initial population generation methods on GA performance,and then improves GA to solve the power system VAR optimization scheduling problem.Based on the analysis of the VAR optimization scheduling model,a VAR optimization algorithm based on GA is constructed.We use GA to encode decision variables in real numbers,use binary competition strategy to select individual offspring,and combine simulated binary crossover(SBX)and polynomial mutation schemes as crossover operators and mutation operators.Four initial population generation methods are evaluated,i.e.,Subtract with Borrow(SWB),Niderreiter Quasi-random Sampling(NQR),Non-Aligned System(NAS)and Gaussian Sampling(GS).Through experiments on the IEEE-30 bus system,the results show that using a uniformly sampled SWB pseudo-random generator to generate the GA of the initial population,the active power loss calculated in the power system VAR optimal scheduling calculation is the lowest,only 4.00%.

关 键 词：无功功率 优化调度 遗传算法 初始种群 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TM714.3[自动化与计算机技术—控制科学与工程]