摘要
研究水轮发电机组PID参数优化问题,由于PID参数是一个多参数组合系统,参数间互相关联,呈高度非线线性关系,传统参数优化算法采用经验方法,耗时长,难以找到最优参数,导致PID控制精度不高。为了获取最优PID参数,提高系统控制精度,提出一种粒子群算法的PID参数优化方法。采PID参数作为粒子群中的一个粒子,PID控制精度作为粒子的目标函数,通过粒子相互作用,不断缩小粒子的搜索空间,通过引入重新启动策略,提高粒子摆脱局部极值能力,最后找到最优PID参数。通过对某水电站的真实数据对参数优化后的PID控制器进行测试实验,结果表明,粒子群优化算法能够很快找到PID最优参数,明显提高了水轮发电机组PID控制精度,系统超调量更小,调节时间更短,为控制系统优化提供了依据。
Research hydro-generating PID parameter optimization problem.PID parameters are highly related with each other,the traditional parameter optimization algorithm is time-consuming and hard to find the optimal parameters,and the PID control accuracy is not high.In order to obtain the optimal PID parameters and improve the system control precision,a PID parameter optimization method was proposed based on particle swarm algorithm.PID parameters were taken as a particle,and particle swarm PID control accuracy was taken as objective function.The particles particles interacted with one another to shrink the searching space of particles,and through introducing the restart strategy,the ability of getting rid local extremum was improved.Finally,the optimal PID parameters were found.The proposed method is tested by the data from a hydropower station.The experimental results show that particle swarm optimization algorithm can quickly find the optimal parameters,which improves hydro-generating PID control precision significantly.
出处
《计算机仿真》
CSCD
北大核心
2011年第10期312-315,共4页
Computer Simulation
关键词
粒子群算法
参数优化
水轮发电机组
调速器
PSO
Parameters optimization
Hydraulic turbogenerators
Speed governor
