摘要
提出了一种多策略融合自适应粒子群优化(MSI-APSO)算法求解电力系统无功优化问题的新方法。该方法采用分阶段调整加速因子,结合适应值自适应调整惯性权重,然后基于群体信息改善部分性能差的粒子,迭代后性能改善的粒子,采取速度保持策略,从而提高了PSO全局寻优性能。针对IEEE30节点系统进行无功优化计算,并与带惯性权重的粒子群(PSO-w)算法、带压缩因子的粒子群(PSO-c)f算法、全面学习粒子群(CLPSO)算法进行了比较,表明MSI-APSO具有更好的全局寻优能力和收敛性能。
Applying adaptive particle swarm optimization with multi-strategy integration （MSI-APSO） algoritm,a new approach of solving reactive power optimization is proposed.The method adjusts the acceleration factor with stage varying,adaptively adjusts inertia weight according to the fitness,then employs group information to improve the particles with the worse performances.For the particles which are improved after iteration,the speed remaining strategy is employed.In this case,the global optimazation performance of PSO is consequently improved.The proposed algorithm is applied to the reactive power optimization of IEEE30-bus system,and compared with PSO-w,PSO-cf,CLPSO,the results indicate that MSI-APSO has better globally optimal capability and convergence performance.
出处
《电力系统及其自动化学报》
CSCD
北大核心
2014年第2期8-13,43,共7页
Proceedings of the CSU-EPSA
基金
国家自然科学基金资助项目(51307144)
关键词
无功优化
智能优化算法
粒子群优化算法
有功网损
reactive power optimization
intelligent optimization algorithm
particle swarm optimization algorithm
active network loss
