基于自抗扰Smith预估补偿方法的超临界机组再热汽温控制研究
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摘要
为了解决超临界火电机组再热汽温波动大、自动难于投入以及喷水量大造成经济性差的问题,将自抗扰(active disturbance rejection controller,ADRC)控制技术与Smith预估补偿控制有机融合,提出了一种适用于存在大滞后与非线性特性的先进再热汽温优化控制策略。对某600 MW超临界机组再热汽温对象进行仿真研究,结果表明:与传统再热汽温控制系统相比,所提出的先进再热汽温控制策略可以减小再热汽温的波动,设定值跟踪性能与抗扰动能力均得到较大改善,同时可以减小喷水量提高机组经济性。
In order to solve the problem of high reheated steam temperature fluctuation, difficult operation of automatic control and low economical efficiency because of high spraying water in supercritical unit,an advanced reheated steam temperature optimal control strategy being applicable to big lag and nonlinear characteristic was put forward by combining the active disturbance rejection controller(ADRC) with the Smith predictive compensation control. Through the simulation of the reheated steam temperature object in 600 MW supercritical unit, the results show that compared with the traditional reheated steam temperature control system, the proposed advanced reheated steam temperature control strategy can decrease the reheated steam temperature fluctuations, setpoint tracking performance and disturbance rejection can be improved, also the economical efficiency can be raised by reducing the amount of spraying water.
In order to solve the problem of high reheated steam temperature fluctuation, difficult operation of automatic control and low economical efficiency because of high spraying water in supercritical unit,an advanced reheated steam temperature optimal control strategy being applicable to big lag and nonlinear characteristic was put forward by combining the active disturbance rejection controller(ADRC) with the Smith predictive compensation control. Through the simulation of the reheated steam temperature object in 600 MW supercritical unit, the results show that compared with the traditional reheated steam temperature control system, the proposed advanced reheated steam temperature control strategy can decrease the reheated steam temperature fluctuations, setpoint tracking performance and disturbance rejection can be improved, also the economical efficiency can be raised by reducing the amount of spraying water.
引文
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[2]郝建刚,金李,车方,等.660 MW超超临界再热汽温偏差分析及优化方案研究[J].发电与空调,2015,36(6):31-34.
[3]马龙信,路昆,葛伟,等.660 MW超超临界锅炉汽温偏差调整试验研究[J].发电与空调,2016,37(2):26-29.
[4]薛虹,韩璞,张妍,等.基于BBO算法的再热汽温混合优化控制[J].计算机仿真,2015,32(11):400-403.
[5]王爽心,贺飞,刘如九,等.基于间接能量平衡的锅炉汽温GPC-PID串级控制[J].电机与控制学报,2016,20(9):9-16.
[6]王佳.预测控制在再热汽温控制中的应用[D].北京:华北电力大学,2017.
[7]崔晓波,陈雨亭,秦文炜,等.多模型块结构Laguerre函数预测控制在再热汽温系统中的应用[J].东南大学学报:自然科学版,2013,43(4):803-808.
[8]王富强,李晓理,张秋生,等.基于Smith控制与预测函数控制的再热汽温多变量控制快速计算方法[J].计算机应用,2015,35(12):3597-3601.
[9]刘劲权.多模型预测控制在超临界机组再热汽温系统中的研究[D].南京:东南大学,2015.
[10]陈俊丞.基于内模原理的超临界机组再热汽温控制系统研究[D].南京:东南大学,2016.
[11]韩京清.从PID技术到“自抗扰控制”技术[J].控制工程,2002,9(3):13-18.
[12]朱红霞.基于多模型的热工过程先进控制策略研究[D].南京:东南大学,2015.
[13]胡连华.线性系统的模型降阶和H?控制器降阶[D].沈阳:东北大学,2010.