邓长虹;马庆;肖永;游佳斌;李世春;

• 1:武汉大学电气工程学院
• 2:贵州电力试验研究院

摘要(Abstract)：

针对粒子群算法在求解无功优化问题时存在早熟收敛,易陷于局部最优的现象,提出了自学习迁移粒子群算法(self-learning migration particle swarm optimization,SLMPSO)。该算法在采用混沌序列对粒子群进行初始化操作,基于云模型理论的X-条件云发生器对粒子的惯性权重进行自适应调整的基础上,引入一种迁移操作,以引导全局最优粒子的飞行方向,解决粒子群后期朝单一进化方向进化的问题,有效地增强了算法的全局寻优能力。针对电力系统无功优化中的离散变量归整问题,首先将离散变量完全化为连续变量进行迭代求解,在寻求至全局最优解后引入高斯罚函数对离散变量进行归整操作。以网损和电压偏离最小为目标,对IEEE标准30节点算例进行仿真计算,验证了所提算法的有效性和可行性。

关键词(KeyWords)： 云模型;迁移操作;粒子群优化算法;高斯罚函数;无功优化

Abstract:

Keywords:

基金项目(Foundation): 国家科技支撑计划(2013BAA02B02)~~

作者(Author): 邓长虹;马庆;肖永;游佳斌;李世春;

Email:

DOI: 10.13335/j.1000-3673.pst.2014.12.011

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