摘要
针对基本粒子群优化参数性能的不足及其优化算法易早熟的弊端,提出一种带压缩因子的二阶粒子群改进算法(CF-Sec PSO)。采用多种测试函数对改进算法进行测试,同时介绍控制器参数优化时目标函数的选取,将算法应用于电厂主汽温控制系统控制器参数优化中。仿真研究表明:与基本粒子群算法和带压缩因子粒子群(CFPSO)算法相比,改进的粒子群算法改善了算法的搜索速度及精度,有效避免陷入局部最优。将其应用于优化主汽温的PID串级控制器参数,改进算法提升了控制系统的性能,对实际控制系统中参数整定提供了重要参考,验证了该算法的适用性。
In order to improve the performance of the optimized parameters of the basic particle swarm optimization and avoid the disadvantages of the optimization algorithm, a new algorithm for the two order particle swarm optimization with compression factor （CF-SecPSO） is proposed. Several test functions are used to test and analyze the algorithm. The accuracy of improved particle swarm algorithm is verified, and the problem of local optimum is avoided. At the same time, the algorithm is applied to optimize the control parameters, compared with the traditional particle swarm algorithm and the compression factor of the panicle swarm algorithm （CFPSO） , and the performance of the PID control system can be greatly improved, which verifies the practicability of the algorithm.
出处
《电力科学与工程》
2017年第2期73-78,共6页
Electric Power Science and Engineering
基金
中央高校基本科研业务费专项资金(2016MS143)
关键词
控制系统
参数优化
PSO
压缩因子
主汽温
control system
optimizated parameter
PSO
compression factor
main steam temperature
