基于帐篷映射的混沌自适应粒子群优化算法在同步发电机励磁控制中的应用  

Application Study of Tent Mapping-Based Chaos Adaptive PSO Algorithm in Excitation Control System of Synchronous Generator

作　　者：贺徽[1] 周建中[1] 寇攀高[1] 张孝远[1] 

机构地区：[1]华中科技大学水电与数字化工程学院,湖北省武汉市430074

出　　处：《电网技术》2011年第6期45-49,共5页Power System Technology

基　　金："十一五"国家科技支撑计划重大项目(2008BAB29B08);国家自然科学基金项目(51079057);科技部水利部公益性行业科研专项基金项目(200701008)~~

摘　　要：针对励磁控制系统中比例–积分–微分控制参数整定难的问题,提出了一种基于帐篷映射的混沌自适应权重粒子群优化算法对控制器参数进行优化,采用2级优化策略,第1级采用自适应权重粒子群优化算法执行全局搜索;第2级采用基于帐篷映射的混沌搜索对第1级的结果执行局部遍历搜索,并通过在粒子群算法中引入自适应权重及在混沌局部搜索中采用帐篷映射的方法对混沌粒子群搜索算法进行改进,解决了常规粒子群算法易陷入局部极值且在迭代后期收敛效率低的问题,在建立励磁控制系统简单模型的基础上,实现同步发电机励磁系统的参数优化控制。仿真研究表明,新方法与常规粒子群方法相比具有更好的收敛速度和精度,能有效改善励磁控制系统空载起励和孤网过渡过程的动态性能。To solve the problem that in excitation control system the parameters of proportional integral differential（PID） controller are difficult to be set,a Tent mapping-based chaos adaptive weight particle swarm optimization（PSO） algorithm is proposed to optimize controller parameters.A two-step optimization strategy is adopted：in the first step the fast adaptive particle swarm optimization（APSO） is used for global search;in the second step the Tent mapping-based chaos local search（CLS） is used to perform local ergodic search for the search result from the first step.By means of leading the adaptive weight into PSO algorithm and adopting Tent-mapping in CLS,the chaos particle swarm search algorithm is improved,thus the problems that conventional particle swarm algorithm is apt to fall into local extremum and in the later stage of iteration the convergence is inefficient are solved,and on the basis of establishing a simple model of excitation control system the parameter optimization control of excitation system for synchronous generator is implemented.The proposed method possesses faster convergence speed and better accuracy,thus both no-load energizing of excitation control system and dynamic performance of transient process in isolated network can be effectively improved.

关 键 词：励磁控制 帐篷映射 混沌 粒子群优化 同步发电机 

分 类 号：TM712[电气工程—电力系统及自动化]