基于差分进化粒子群算法的多目标无功优化  

Particle Swarm Optimization Based on Differential Evolution for Multi-Objective Reactive Power Optimization

作　　者：简献忠[1] 李莹[1] 范建鹏[1] 柏勰文 杨延安[2] 

机构地区：[1]上海理工大学电气工程系,上海200093 [2]中国电子科技集团公司第二十三研究所,上海200090

出　　处：《控制工程》2015年第1期113-117,共5页Control Engineering of China

基　　金：国家科技部政府间科技合作项目(2009014);上海市研究生创新基金项目(JWCXSL1302)

摘　　要：针对电力系统有功网损最小、电压水平最好和电压稳定裕度最大的多目标无功优化问题,提出一种基于差分进化的改进多目标粒子群优化算法。该算法通过对Pareto最优解集的差分进化来增加Pareto最优解的多样性,通过拥挤距离来控制精英集中非支配解的分布,以提高对种群空间的均匀采集;采用擂台赛法则构造多目标Pareto最优解集,较大程度的提高了算法的运行效率;自适应惯性权重和加速度因子的动态变化可增强算法的全局搜索能力。将该算法在IEEE14、IEEE30节点标准测试系统上进行了无功优化仿真,结果表明,基于差分进化的改进多目标粒子群优化算法能够在保持Pareto最优解的多样性的同时具有较好的收敛性能,为多目标无功优化提供了一种新的方法。In allusion to multi-objective reactive power optimization related to active network loss minimization, high voltage quality and voltage stability margin maximization are taken into account, an improved multi-objective particle swarm optimization based on differential evolution technique is proposed to solve the multi-objective reactive power optimization problem. The algorithm adopts differential evolution to increase the diversity of the Pareto set.Crowded sorting approach helps to generate a set of well-distributed Pareto-optimal solutions in one run. An approach of constructing multi-objective Pareto optimal solutions uses the arena’s principle which can improve operation efficiency. And the adaptive inertia weight and acceleration coefficients enhance the global exploratory capability. The improved algorithm was implemented on the IEEE-14 bus and IEEE-30 bus system . The results indicate that the algorithm can maintain the diversity of Pareto-optimal solutions and has better convergency at the same time.

关 键 词：无功优化 多目标 差分进化 粒子群优化算法 非支配排序 

分 类 号：TM714.3[电气工程—电力系统及自动化] TP301.6[自动化与计算机技术—计算机系统结构]