[1]方红庆.一种改进粒子群算法及其在水轮机控制器PID参数优化中的应用[J].南京理工大学学报(自然科学版),2008,(03):274-278.
 FANG Hong-qing.Improved Particle Swarm Optimization Algorithm and Its Application in Hydraulic Turbine Governor PID Parameters Optimization[J].Journal of Nanjing University of Science and Technology,2008,(03):274-278.
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一种改进粒子群算法及其在水轮机控制器PID参数优化中的应用
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
期数:
2008年03期
页码:
274-278
栏目:
出版日期:
2008-06-30

文章信息/Info

Title:
Improved Particle Swarm Optimization Algorithm and Its Application in Hydraulic Turbine Governor PID Parameters Optimization
作者:
方红庆;
河海大学电气工程学院, 江苏南京210098
Author(s):
FANG Hong-qing
College of Electrical Engineering,Hohai University,Nanjing 210098,China
关键词:
粒子群优化 群智能 水轮机控制器 参数优化
Keywords:
particle swarm optimization swarm intelligence hydraulic turbine governor parameters optimization
分类号:
TP273
摘要:
提出一种改进的粒子群优化算法,除了个体极值和全局极值外,改进算法中还引入了粒子群的平均位置。因此,粒子可以获得更多的信息来调整自身的状态。基于3个基准测试函数的测试结果显示改进粒子群优化算法具有较好的全局收敛性和收敛精度。计算机仿真结果表明:改进粒子群优化算法应用于水轮机控制器PID参数的优化设计可以有效地改善水轮机控制系统过渡过程的动态性能。
Abstract:
An improved particle swarm optimization(PSO) algorithm is presented.Besides the individual best position and the global best position,the swarm average position is introduced in the improved PSO algorithm.Therefore,more information is acquired by particles to adjust their movements.The test results based on three benchmark functions show that the improved PSO algorithm has a good performance on global convergency and convergence precision.The computer simulation results indicate that the application of the improved PSO algorithm in hydraulic turbine governor PID parameters optimization can effectively improve the dynamic performance of hydraulic transients.

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备注/Memo

备注/Memo:
基金项目:河海大学自然科学基金(2007418211) ??作者简介:方红庆(1975-),男,副教授,博士,河南开封人,主要研究方向:水力机组控制及其过渡过程、非线性 控制、进化计算、群智能、电站自动化与仿真技术等,E_mail:fanghongqing@sina.com。
更新日期/Last Update: 2008-06-30