摘要
针对火电机组燃煤锅炉系统的大惯性、大滞后和强耦合等特点,提出了一种基于隐式广义预测控制(Implicit generalized predictive control,IGPC)的火电机组多目标协同燃烧优化控制策略。以主蒸汽压力、炉膛负压和烟气含氧量为被控量,以燃料量、引风量和送风量为操纵量,设计基于IGPC解耦控制的多输入多输出燃烧优化控制系统,通过PID神经网络对多输入多输出预测模型进行解耦,利用滚动优化实时对目标函数进行寻优,并引入基于误差的反馈校正算法,使各控制量输出达到设定值。仿真结果表明:IGPC解耦控制策略与常规PID和IGPC未解耦控制策略相比,施加扰动的情况下,主汽压、烟气含氧量和炉膛负压调节时间分别最多可减少141s、210s和162s,超调量分别最多可减少18.2%、4%和9.3%。工程应用表明:主汽压、烟气含氧量和炉膛负压控制偏差分别低于10.15MPa、3%和±0.7Pa,各被控量波动范围小,具有良好的控制效果。
Aiming at the characteristics of large inertia,large lag and strong coupling of coal-fired boiler systems of thermal power units,a multi-objective cooperative combustion optimization control strategy for thermal power units based on implicit generalized predictive control(IGPC) was proposed.Taking main steam pressure,furnace negative pressure and flue gas oxygen content as the controlled quantities,and taking fuel quantity,induced air volume and air supply volume as control quantities,a multi-input multi-output combustion optimization control system based on IGPC decoupling control was designed.The PID neural network decouples the multi-input multi-output prediction model,uses rolling optimization to optimize the objective function in real time,and introduces an error-based feedback correction algorithm to make the output of each control variable reach the set value.The simulation results show that compared with the conventional PID and IGPC undecoupled control strategies,the IGPC decoupling control strategy can reduce the adjustment time of main steam pressure,flue gas oxygen content and furnace negative pressure by up to 141 s,210 s and 162 s respectively under the condition of disturbance.The overshoot can be reduced by up to 18.2%,4% and 9.3% respectively.The engineering application shows that the control deviation of main steam pressure,flue gas oxygen content and furnace negative pressure are lower than±0.15 MPa,3% and ±0.7 Pa respectively;and the fluctuation range of each controlled quantity is small,which has a good control effect.
作者
冯旭刚
鲍立昌
章家岩
王胜
FENG Xugang;BAO Lichang;ZHANG Jiayan;WANG Sheng(Anhui University of Technology,Maanshan 243032,Anhui Province,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2021年第9期3223-3231,共9页
Proceedings of the CSEE
基金
安徽省自然科学基金(1908085ME134)
安徽省重点研发与开发计划项目(1804a09020094)
安徽省高校自然科学研究重点项目(KJ2018A0060)。
关键词
多输入多输出
隐式广义预测解耦控制
多目标协同控制
燃烧优化
multiple input multiple output
implicit generalized predictive decoupling control
multiple-objective and cooperative control
combustion optimization
