摘要
针对遗传算法(SGA)存在求解精度与收敛速度间的矛盾,提出了一种自适应对称调和遗传算法(IASHGA),并将该算法用于水轮发电机组PID调速系统参数的优化设计,以系统的上升时间和超调量指标作为IASHGA算法的适应度函数;以四川某水电站的真实数据对经过优化后遗传算法PID控制规律进行计算机仿真.仿真结果表明,改进的算法较之常规遗传算法(SGA)和粒子群优化算法(PSO),不但提高了全局的搜寻能力,而且有效避免了早熟收敛问题.为水轮机调速器PID参数优化研究提供了新途径.
In allusion to the conflict of solving precision and convergence speed in simple genetic algorithm （SGA）, this paper proposes a kind of a genetic algorithm for self adaptation, symmetry and congruity and also applies it to the optimization of the PID gains tuning of hydraulic turbogenerators speed governor. The rise time and overshoot volume targets are taken as the fitness functions of the IASHGA algorithm. The optimized PID control law is simulated with data from a hydropower plant in Sichuan. The experimental results show that the improved method not only has better ability to converge to the global optimum than the SGA and the PSO, but also can avoid the premature convergence effectively. It is a new way to optimize the turbine governor PID parameter.
出处
《昆明理工大学学报（理工版）》
北大核心
2009年第3期66-70,共5页
Journal of Kunming University of Science and Technology(Natural Science Edition)
基金
四川省教育厅科学研究基金项目(项目编号:2006C095)
成都市科技攻关项目(项目编号:07GGYB198SF)
关键词
对称调和
遗传算法
水轮发电机组
调速器
PID参数优化
symmetry and congruity
genetic algorithm
hydraulic turbogenerator
speed governor
PID optimization
