BF-PSO算法在电网CPS标准经济考核中的应用  

Application of the Synthesis of Bacterial Foraging Algorithm and Particle Swarm in Power Grid CPS Economic Assessment

作　　者：严文洁[1,2] 雷霞[1] 李挺[3] 罗皓文 杨毅[1] 

机构地区：[1]西华大学电气信息学院,成都610039 [2]湖北省荆门市供电公司,荆门448000 [3]四川省电力公司广安电业局,广安638000

出　　处：《电力系统及其自动化学报》2014年第10期7-12,共6页Proceedings of the CSU-EPSA

基　　金：国家自然科学基金项目(51007006);四川省教育厅重点项目(11za002);西华大学2012人才培养项目(ycjj201247);电力电子节能技术与装备重点实验室开放基金(szjj2014-015)

摘　　要：对于如何减少调度控制性能标准CPS(control performance standard)的考核罚款及机组的运行成本,提出了一种基于混合粒子群优化的细菌觅食算法BF-PSO(bacterial foraging-particle swarm optimization)自整定PI控制。文中首先详细介绍了粒子群算法和细菌觅食算法的基本原理,在结合两种算法的基础上,以发电成本和考核罚款之和最小为目标提出了一种新的PI整定方法,通过Matlab的Simulink仿真软件建立了一个多区域电力系统的CPS考核系统模型,对优化前后的控制效果进行了对比,仿真结果说明新的控制方法在保持CPS控制效果的基础上,实现了对电网的经济调度。To reduce penalty due to CPS assessment failure in power grids and operating costs of generation units,a norel self-adjusted PI optimization control method is proposed based on the synthesis of bacterial foraging algorithm and particle swarm optimization.In this paper,particle swarm optimization（PSO） and bacterial foraging algorithm are introduced.On the basis of the combination of these two algorithms,a new self-adjusted PI optimization control method which takes the minimal sum of CPS assessment failure penalty and costs of units as object is put forward.Via simulating software Matlab （Simulink）,a multi-area control model of power grids is constructed.According to the results,the new control method not only keeps the CPS control performance but also realizes economic dispatch.

关 键 词：控制性能标准 粒子群优化 细菌觅食算法 经济调度 

分 类 号：TM76[电气工程—电力系统及自动化]