基于云自适应梯度粒子群算法的无功优化Reactive Power Optimization Based on Cloud Adaptive Gradient Particle Swarm Optimization
祝洪博;徐刚刚;海冉冉;余立平;
摘要(Abstract):
粒子群算法存在着早熟的现象,易陷入局部最小点,为了克服这个缺点,文章首先将云模型引入粒子群算法,将粒子分成2部分,靠近最优粒子和远离最优粒子的部分,其中靠近最优粒子种群的惯性权重由云模型的X-条件发生器自适应调整,提出了云自适应粒子群算法(cloud adaptiveparticle swarm optimization,CAPSO),然后引入梯度的思想,提出云自适应梯度粒子群算法(cloud adaptive gradientparticle swarm optimization,CAGPSO)。以网损最小为目标函数,对标准IEEE 14和IEEE 30节点系统进行仿真计算,结果表明改进后的CAGPSO算法能够获得更好的优化解。
关键词(KeyWords): 云理论;网损最小;云自适应梯度粒子群算法;无功优化
基金项目(Foundation):
作者(Author): 祝洪博;徐刚刚;海冉冉;余立平;
Email:
DOI: 10.13335/j.1000-3673.pst.2012.03.031
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