基于精准能量平衡与预测控制的协调控制系统优化
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摘要
我国火电机组面临煤质煤种多变的问题,随着电网对AGC、一次调频要求的不断提高,火电机组需进一步提升机炉协调控制系统的适应能力。超超临界机组具有高效节能的特点,是目前提高火电机组热效率的有效途径,以超超临界机组机炉协调控制系统作为研究对象,构建了超超临界机组非线性控制模型以及入炉煤低位发热量的软测量方法,实现了锅炉输入热量、锅炉动态蓄能和汽机侧能量需求的精准平衡;并采用阶梯式预测控制算法取代常规的PID控制器,将前馈和解耦的控制思想融入预测控制算法中,进一步克服了系统强耦合、大惯性、大迟延特性对调节性能的影响,实现了机组在高效宽负荷范围的高效灵活运行。仿真结果表明,加入入炉煤低位发热量作为控制器前馈后,主蒸汽压力、中间点焓值、主汽阀开度等参数均能快速响应滑压运行需求,且有效降低被控参数的超调量;当煤质发生变化时,控制器能迅速调节给煤量,各参数能快速回归设定值,且超调量较小;基于精准能量平衡的阶梯式预测协调控制策略可以有效抑制煤质扰动及锅炉蓄能变化对机炉调节性能的影响,从而进一步提升主汽压力调节的快速性和平稳性。
China's thermal power units are faced with many problems, such as the variety of coal quality and coal type, as well as the increasing requirements for AGC and primary frequency modulation in the power grid. Thermal power units need to further improve the adaptability of the boiler-turbine coordination system. Ultra-supercritical units characterized by high efficiency and energy saving are an effective way to improve the thermal efficiency of thermal power units. The author takes the boiler-turbine coordinated control system of ultra-supercritical units as the research object. The nonlinear control model of the units and the soft-sensing methods of low calorific value of coal entering the boiler and dynamic energy storage of the boiler are studied to realize the accurate balance of boiler input heat, dynamic energy storage of the boiler and energy demand on the turbine side. The stepped predictive control algorithm is used to replace the conventional PID controller, and the feedforward as well as decoupling control is integrated into predictive control algorithm to further. The algorithm overcomes the influence of strong coupling, large inertia and large delay characteristics of the system on the regulation performance, and achieves efficient and flexible operation of the unit in a high-efficiency and wide load range. The simulation shows that the main steam pressure, intermediate point enthalpy and main steam valve opening degree can quickly respond to sliding pressure operation after adding the low calorific value of coal as feedforward of the controller, and the overshoot is smaller than that without feedforward. The controller can quickly adjust the coal feeding amount, and each parameter can quickly return to the set value with small overshoot when the coal quality changes. The result show that the stepped predictive control strategy based on accurate energy balance can effectively inhibit the influence of coal quality disturbance and boiler energy storage change on the regulation performance of the turbine and boiler, and further improve the rapidity and stability of main stem pressure regulation.
China's thermal power units are faced with many problems, such as the variety of coal quality and coal type, as well as the increasing requirements for AGC and primary frequency modulation in the power grid. Thermal power units need to further improve the adaptability of the boiler-turbine coordination system. Ultra-supercritical units characterized by high efficiency and energy saving are an effective way to improve the thermal efficiency of thermal power units. The author takes the boiler-turbine coordinated control system of ultra-supercritical units as the research object. The nonlinear control model of the units and the soft-sensing methods of low calorific value of coal entering the boiler and dynamic energy storage of the boiler are studied to realize the accurate balance of boiler input heat, dynamic energy storage of the boiler and energy demand on the turbine side. The stepped predictive control algorithm is used to replace the conventional PID controller, and the feedforward as well as decoupling control is integrated into predictive control algorithm to further. The algorithm overcomes the influence of strong coupling, large inertia and large delay characteristics of the system on the regulation performance, and achieves efficient and flexible operation of the unit in a high-efficiency and wide load range. The simulation shows that the main steam pressure, intermediate point enthalpy and main steam valve opening degree can quickly respond to sliding pressure operation after adding the low calorific value of coal as feedforward of the controller, and the overshoot is smaller than that without feedforward. The controller can quickly adjust the coal feeding amount, and each parameter can quickly return to the set value with small overshoot when the coal quality changes. The result show that the stepped predictive control strategy based on accurate energy balance can effectively inhibit the influence of coal quality disturbance and boiler energy storage change on the regulation performance of the turbine and boiler, and further improve the rapidity and stability of main stem pressure regulation.
引文
[1]李露. 火电机组快速变负荷控制方法研究[D]. 北京:华北电力大学,2016.
[2]胡勇. 基于汽轮机蓄能特性的大型火电机组快速变负荷控制研究[D]. 北京: 华北电力大学,2015.
[3]闫姝. 超超临界机组非线性控制模型研究[D]. 北京:华北电力大学,2013.
[4]王富强,李晓理,张秋生,等.直流锅炉超超临界机组协调系统多变量预测控制方法[J].热力发电,2016,45(4):48-53.
[5]李勇, 许海雷. 凝结水节流参与的1000MW二次再热机组一次调频控制方法[J]. 电力科技与环保, 2018,34(6):34-36.
[6]赵征. 基于信息融合的锅炉燃烧状态参数检测技术研究[D]. 河北:华北电力大学, 2007.
[7]曾德良,闫姝,孙伟毅,等.直流炉机组燃煤发热量信号测量模型[J].动力工程学报,2012,32(7):528-531+537.
[8]席裕庚.预测控制(第二版)[M].北京:国防工业出版社,2013.
[9]Tao Z, Gang W, He D, et al. An efficient model nonlinear predictive control algorithm based on stair-like control strategy[C]. Control Conference. 2008.
[10]Qiu X, Xue M, Sun D, et al. The stair-like generalized predictive control for main-steam pressure of boiler in steam-power plant[C]. Intelligent Control and Automation, 2000. Proceedings of World Congress on. IEEE Xplore, 2000(5):3165-3167.
[11]罗国娟. 阶梯式预测控制器的参数整定研究[D]. 合肥:中国科学技术大学, 2006.
[12]孙翼. 预测控制在单元机组协调控制系统中的应用研究[D]. 北京:华北电力大学, 2011.
[13]高昊天, 范浩杰, 董建聪, 等. 超超临界二次再热机组的发展[J]. 锅炉技术, 2014, 45(4):1-3.
[14]蔡宝玲, 高海东, 王剑钊, 等. 二次再热超超临界机组动态特性分析及控制策略验证[J]. 中国电机工程学报, 2016, 36(19):5288-5299.
[15]梁庆姣, 刘吉臻, 薛彦广,等. 超超临界机组的非线性模型及动态特性研究[J]. 动力工程学报, 2012, 32(2):118-123.
[16] 田亮, 练海晴, 刘鑫屏, 等. 直流锅炉蒸汽压力与中间点温度耦合特性分析[J]. 中国电机工程学报, 2017(4):201-210.
[17] 秦志明, 刘吉臻, 张栾英, 等. 超临界直流锅炉蓄热的分析与计算[J]. 动力工程学报, 2013, 33(4):250-255.
[18]邓拓宇. 燃烧过程状态重构及在协调控制系统优化中的应用[D]. 华北电力大学, 2012.
[19]李培, 梁增同, 高振罡, 等. 某电厂600MW机组锅炉掺烧劣质煤制粉系统优化调整试验研究[J]. 热力发电, 2013, 42(5):64-68.
[20]李永生, 谭锐, 徐星. 1000MW超超临界二次再热机组滑压运行优化[J]. 电力科技与环保, 2018,34(3):56-58.
[2]胡勇. 基于汽轮机蓄能特性的大型火电机组快速变负荷控制研究[D]. 北京: 华北电力大学,2015.
[3]闫姝. 超超临界机组非线性控制模型研究[D]. 北京:华北电力大学,2013.
[4]王富强,李晓理,张秋生,等.直流锅炉超超临界机组协调系统多变量预测控制方法[J].热力发电,2016,45(4):48-53.
[5]李勇, 许海雷. 凝结水节流参与的1000MW二次再热机组一次调频控制方法[J]. 电力科技与环保, 2018,34(6):34-36.
[6]赵征. 基于信息融合的锅炉燃烧状态参数检测技术研究[D]. 河北:华北电力大学, 2007.
[7]曾德良,闫姝,孙伟毅,等.直流炉机组燃煤发热量信号测量模型[J].动力工程学报,2012,32(7):528-531+537.
[8]席裕庚.预测控制(第二版)[M].北京:国防工业出版社,2013.
[9]Tao Z, Gang W, He D, et al. An efficient model nonlinear predictive control algorithm based on stair-like control strategy[C]. Control Conference. 2008.
[10]Qiu X, Xue M, Sun D, et al. The stair-like generalized predictive control for main-steam pressure of boiler in steam-power plant[C]. Intelligent Control and Automation, 2000. Proceedings of World Congress on. IEEE Xplore, 2000(5):3165-3167.
[11]罗国娟. 阶梯式预测控制器的参数整定研究[D]. 合肥:中国科学技术大学, 2006.
[12]孙翼. 预测控制在单元机组协调控制系统中的应用研究[D]. 北京:华北电力大学, 2011.
[13]高昊天, 范浩杰, 董建聪, 等. 超超临界二次再热机组的发展[J]. 锅炉技术, 2014, 45(4):1-3.
[14]蔡宝玲, 高海东, 王剑钊, 等. 二次再热超超临界机组动态特性分析及控制策略验证[J]. 中国电机工程学报, 2016, 36(19):5288-5299.
[15]梁庆姣, 刘吉臻, 薛彦广,等. 超超临界机组的非线性模型及动态特性研究[J]. 动力工程学报, 2012, 32(2):118-123.
[16] 田亮, 练海晴, 刘鑫屏, 等. 直流锅炉蒸汽压力与中间点温度耦合特性分析[J]. 中国电机工程学报, 2017(4):201-210.
[17] 秦志明, 刘吉臻, 张栾英, 等. 超临界直流锅炉蓄热的分析与计算[J]. 动力工程学报, 2013, 33(4):250-255.
[18]邓拓宇. 燃烧过程状态重构及在协调控制系统优化中的应用[D]. 华北电力大学, 2012.
[19]李培, 梁增同, 高振罡, 等. 某电厂600MW机组锅炉掺烧劣质煤制粉系统优化调整试验研究[J]. 热力发电, 2013, 42(5):64-68.
[20]李永生, 谭锐, 徐星. 1000MW超超临界二次再热机组滑压运行优化[J]. 电力科技与环保, 2018,34(3):56-58.