基于粒子群布谷鸟融合算法的主汽温系统控制器参数优化

Parameter optimization of main steam temperature system controllerbased on PSO cuckoo fusion algorithm

火电厂主汽温系统具有大惯性、大时滞特性,经典串级比例-积分-微分控制器(PID)难以实现对主汽温系统的精细控制,本文利用粒子群与布谷鸟的融合算法对主汽温控制系统的控制器参数进行优化,通过选择合适的目标函数,对某600 MW直流锅炉主汽温控制系统进行了优化,与工程整定法、粒子群算法的控制结果相比,粒子群布谷鸟融合算法具有更好的全局寻优特性,具有更好的动态性能与抗干扰能力.

The main steam temperature system of power plant has the characteristics of large inertia and large time delay.The classical cascade PID control is difficult to implement the precise control of the main steam temperature system.In this paper,the fusion algorithm of particle swarm optimization and cuckoo is used to optimize the controller parameters of the main steam temperature control system.By selecting the appropriate objective function,the main steam temperature control system of a 600 MW once-through boiler is optimized.Compared with the control results of PSO algorithm and practical tuning method,PSO-cuckoo fusion algorithm has better global optimization characteristics,better dynamic performance and anti-interference ability.