基于改进云粒子群算法的电力系统无功优化研究  

Research on power system reactive power optimization based on improved cloud particle swarm algorithm

作　　者：张佩炯[1] 苏宏升[1] 李晓青[1] 张吉斌[1] 

机构地区：[1]兰州交通大学自动化与电气工程学院,甘肃兰州730070

出　　处：《电气传动自动化》2013年第3期1-6,共6页Electric Drive Automation

基　　金：甘肃省普通高等学校研究生导师资助项目(No.1004-06)

摘　　要：针对云粒子群算法(CPSO)在电力系统无功优化中易陷入局部极值,也存在早熟收敛问题,将基于云数字特征(期望值、熵值、超熵值)编码的云粒子群算法进行了改进:依据解空间的变换将局部搜索和全局搜索相结合,用正态云算子实现粒子的进化学习和交叉变异操作。改进的算法在时间、存储量性能上有了明显的提高,将改进后的算法应用到IEEE30节点标准测试系统和电网中进行仿真运算,与其它算法进行比较。结果表明,该方法在配电网无功优化中能取得更好的全局最优解,加快了收敛速度,提高了收敛精度。In view of cloud particle swarm optimization is easily trapped in local minimum and existed slowly convergence in the power system reactive power optimization, the cloud particle swarm optimization is improved based on cloud digital features （Ex, En, He）. The local search and global search based on the solution space transform are combined, the evolution of the learning process and the variation operation according to the normal cloud particle are achieved. The apparent improvement are got in time and storage by using the algorithm, and the simulation experiment are done in IEEE30-bus system and a certain power network. The simulation results show that a better global solution can be attained by using the improved algorithm, convergence speed and accuracy are accelerated and improved.

关 键 词：电力系统 无功优化 云粒子群算法 云模型 

分 类 号：TM741.3[电气工程—电力系统及自动化]