摘要
生物地理学优化(BBO)算法是一种较好的全局优化算法,具有设置参数少、计算简单、收敛速度快等优点。针对超(超)临界机组烟气-再热汽温对象具有大惯性及滞后特性,传统PID控制器参数整定困难的特点,给出了一种基于BBO的混合优化方法。方法针对定值扰动、内部扰动及外部扰动进行综合优化,优化的PID控制器具有不但具有较好的定值跟踪能力还具备较好的抗击内外干扰的性能。仿真结果证明,上述算法优化的PID控制器参数对烟气-再热汽温系统的控制是可行且有效的。
Biogeography-Based Optimization （BBO） algorithm is a better global optimization algorithm. Compared with traditional algorithms, BBO algorithm has the advantages of less parameters, simple calculation, fast convergence. The supercritical （uhra-supercritical） unit flue-reheat steam temperature object has the characteristics such as large inertia and hysteresis. Aiming at this problem, the article put up a hybrid optimization method based on BBO algorithm. The method has a significant comprehensive optimization result in the test of value disturbance, internal disturbances and external disturbance. The optimized PID controller also has better tracking ability and a better performance to fight inside and outside interference. The simulation results show that it is feasible and effective for the control of temperature by using the optimized PID controller on flue-reheat steam control system.
出处
《计算机仿真》
CSCD
北大核心
2015年第11期400-403,共4页
Computer Simulation
基金
中央高校基本科研业务费专项资金自助项目(2014MS139)
关键词
生物地理学优化算法
再热汽温
优化控制
仿真研究
Biogeography-based optimization algorithm
Reheat steam temperature
Hybrid optimization
Simulation study
