摘要
火电机组深调峰工况运行控制难度加剧,其无自平衡能力对象控制尤为困难,针对此问题利用非线性简化动态模型推导得到机组无自平衡对象传递函数,从简单一阶无自平衡对象入手,逐步分析出亚临界机组机侧闭环无自平衡对象炉侧控制器分别采用PD控制和PID控制的适应工况,利用模糊神经网络自学习能力设计新的控制器。仿真实验表明此控制器能够解决亚临界机组无自平衡对象难以人工确定负荷扰动时积分切换控制的难题,运行结果能够满足机组运行要求。
The thermal power units is faced with difficulties in operation control under deep peaking conditions,especially the control of objects without self-balancing ability.Aimed at the units without self-balancing objects,we made use of a nonlinear simplified dynamic model to derive its transfer function.Starting with simple first-order objects without self-balancing,we found out that in the subcritical units,the loop-closed controller at furnace side adopts PD control and PID control to adapt to working conditions.We then designed a new controller by self-learning fuzzy neural network and tested it by simulation experiments.The results show that the new controller realizes integral switching control under load disturbance when sub-critical units have no self-balancing object,thereof meeting the unit operation requirements.
作者
刘暑辉
田亮
LIU Shuhui;TIAN Liang(School of Control and Computer Engineering,North China Electric Power University,Baoding 071003,China)
出处
《华北电力大学学报（自然科学版）》
CAS
北大核心
2021年第1期69-75,共7页
Journal of North China Electric Power University：Natural Science Edition
关键词
亚临界机组
无自平衡对象
模糊神经网络
PID控制器
sub-critical unit
object incapable of self-balance
fuzzy neural network
PID controller
