基于多变量广义预测控制算法的两级过热汽温协调联动控制

doi:10.11930/j.issn.1004-9649.201909059

中国电力 ›› 2020, Vol. 53 ›› Issue (10): 215-223.DOI: 10.11930/j.issn.1004-9649.201909059

基于多变量广义预测控制算法的两级过热汽温协调联动控制

仝声¹, 刘乐², 王鹏飞²

1. 国电电力发展股份有限公司，北京 100101;
2. 北京国电智深控制技术有限公司，北京 100085

收稿日期:2019-09-10 修回日期:2019-09-20 发布日期:2020-10-05
作者简介:仝声(1963—)，男，硕士，高级工程师，从事火电厂关键控制技术、新技术发展前景研究及应用工作，E-mail：sheng.tong.a@chnenergy.com.cn;刘乐(1981—)，男，硕士，工程师，从事电气控制与信息化研究，E-mail：12051609@chnenergy.com.cn;王鹏飞(1991—)，男，硕士，工程师，从事电站自动控制与运行优化研究，E-mail：12053875@chnenergy.com.cn
基金资助:
北京市科学技术委员会能源领域技术协调创新项目（火电机组智能运行控制系统研制，Z181100005118005）

Coordinative Two-Stage Superheated Steam Temperature Linkage Control Based on Multivariable Generalized Predictive Control Algorithm

TONG Sheng¹, LIU Le², WANG Pengfei²

1. Guodian Electric Power Development Co., Ltd., Beijing 100101, China;
2. Beijing Guodian Zhishen Control Technology Co., Ltd., Beijing 100085, China

Received:2019-09-10 Revised:2019-09-20 Published:2020-10-05
Supported by:
This work is supported by the project of Technology Collaborative Innovation in Energy Field of Beijing Municipal Science & Technology Commission (Development of Intelligent Operation Control System for Thermal Power Unit, No.Z181100005118005)

摘要/Abstract

摘要： 大型火力发电机组过热汽温被控对象具有大迟延、大惯性、受干扰因素多以及不同负荷下的被控对象模型变化大等特性，同时面临着煤质多变、环境多变等各种复杂恶劣的工况，以传统PID控制和单变量广义预测控制为基础的汽温控制效果有限。利用递推最小二乘法（recursive least square，RLS）建立了过热汽温系统3个典型工况下的模型，并基于多变量广义预测控制构建了两级过热汽温联动控制优化策略，同时将该策略应用于某电厂330 MW亚临界机组。结果表明，无论是稳态工况还是复杂的变负荷工况，优化控制策略都能够很好地控制过热器出口汽温。

关键词: 火力发电机组, 过热汽温, 多模型, 多变量广义预测控制, 两级联动控制

Abstract: In addition to their own characteristics such as long duration of time delay and large inertia, the controlled objects of superheated steam temperature in large thermal power generating units are affected by a considerable number of factors and model variations under different loading conditions. Especially with complicate coal quality or under volatile environment, the effect of superheated steam temperature control turns out to be very limited, which are based on traditional PID control and single variable generalized predictive control. In this paper, the models of superheated steam temperature system under three typical working conditions are established by virtue of recursive least square method, and then an optimization strategy of two-stage superheated steam temperature linkage control based on multivariable generalized predictive control (MGPC) is constructed. Meanwhile, the strategy is applied to the 330 MW subcritical units in a power plant. The practice results show that the optimal control strategy is effective to control the outlet steam temperature of superheater whenever the system is operated under stable loads or complicated variable loads.

Key words: thermal power generating unit, superheated steam temperature, multi-model, multivariable generalized predictive control (MGPC), two-stage linkage control

仝声, 刘乐, 王鹏飞. 基于多变量广义预测控制算法的两级过热汽温协调联动控制[J]. 中国电力, 2020, 53(10): 215-223.

TONG Sheng, LIU Le, WANG Pengfei. Coordinative Two-Stage Superheated Steam Temperature Linkage Control Based on Multivariable Generalized Predictive Control Algorithm[J]. Electric Power, 2020, 53(10): 215-223.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201909059

https://www.electricpower.com.cn/CN/Y2020/V53/I10/215

参考文献

[1] 谢七月, 周超. 基于改进自适应GPC的锅炉主蒸汽温度预测控制[J]. 热能动力工程, 2018, 33(12): 86-92
XIE Qiyue, ZHOU Chao. Predictive control for the boiler main steam temperature based on improved adaptive generalized predictive control[J]. Journal of Engineering for Thermal Energy and Power, 2018, 33(12): 86-92
[2] 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
WANG Shuangxin, HE Fei, LIU Rujiu, et al. GPC-PID cascade control strategy based on indirect energy balance method for boiler steam temperature system[J]. Electric Machines and Control, 2016, 20(9): 9-16
[3] 王松鹤, 丁维明, 王伟. 过热汽温的非线性广义预测控制与优化[J]. 动力工程学报, 2015, 35(9): 733-739
WANG Songhe, DING Weiming, WANG Wei. Nonlinear general predictive control and optimization of superheat steam temperatures[J]. Journal of Chinese Society of Power Engineering, 2015, 35(9): 733-739
[4] 周洪煜, 高鹏飞, 陈孜虎, 等. 基于神经网络的过热汽温控制[J]. 微计算机信息, 2010, 26(16): 4-5, 11
ZHOU Hongyu, GAO Pengfei, CHEN Zihu, et al. Control for superheated steam temperature based on neural network[J]. Microcomputer Information, 2010, 26(16): 4-5, 11
[5] 刘翠花, 陈红, 王广军. 基于分散推理结构的过热汽温模糊控制及仿真[J]. 系统仿真学报, 2011, 23(7): 1437-1440
LIU Cuihua, CHEN Hong, WANG Guangjun. Fuzzy control and simulation based on distributed inferential structure for superheated temperature[J]. Journal of System Simulation, 2011, 23(7): 1437-1440
[6] 赵慧荣, 沈炯, 沈德明, 等. 主汽温多模型扰动抑制预测控制方法[J]. 中国电机工程学报, 2014, 34(32): 5763-5770
ZHAO Huirong, SHEN Jiong, SHEN Deming, et al. Multiple model disturbance rejection predictive control of main steam temperature[J]. Proceedings of the CSEE, 2014, 34(32): 5763-5770
[7] 邓自立. 最优估计理论及其应用[M]. 哈尔滨: 哈尔滨工业大学出版社, 2005: 48-57.
[8] 吕游, 刘吉臻, 赵文杰, 等. 基于分段曲线拟合的稳态检测方法[J]. 仪器仪表学报, 2012, 33(1): 194-200
LV You, LIU Jizhen, ZHAO Wenjie, et al. Steady-state detecting method based on piecewise curve fitting[J]. Chinese Journal of Scientific Instrument, 2012, 33(1): 194-200
[9] CLARKE D W, MOHTADI C, TUFFS P S. Generalized predictive control: part I The basic algorithm[J]. Automatica, 1987, 23(2): 137-148.
[10] CLARKE D W, MOHTADI C, TUFFS P S. Generalized predictive control: part II Extensions and interpretations[J]. Automatica, 1987, 23(2): 149-160.
[11] 胡耀华, 贾欣乐. 具有约束条件的船舶运动预测控制[J]. 控制理论与应用, 2000, 17(4): 542-547
HU Yaohua, JIA Xinle. Generalized predictive control of ships subject to state constraints[J]. Control Theory & Applications, 2000, 17(4): 542-547
[12] 王建国, 苟晓卫. 基于在线辨识的锅炉中间点温度GPC-PID控制[J]. 控制工程, 2013, 20(4): 631-634
WANG Jianguo, GOU Xiaowei. GPC-PID control for boiler intermediate point temperature based on online identification[J]. Control Engineering of China, 2013, 20(4): 631-634
[13] 彭艳, 吴伟清, 刘梅, 等. 无人艇航迹跟踪GPC-PID串级控制[J]. 控制工程, 2014, 21(2): 245-248
PENG Yan, WU Weiqing, LIU Mei, et al. USV tracking control based on cascade GPC-PID[J]. Control Engineering of China, 2014, 21(2): 245-248
[14] 董晓坤, 方勇纯, 张雪波. 原子力显微镜系统广义预测控制与成像[J]. 控制理论与应用, 2015, 32(8): 1058-1063
DONG Xiaokun, FANG Yongchun, ZHANG Xuebo. Generalized predictive control and imaging for atomic force microscope systems[J]. Control Theory & Applications, 2015, 32(8): 1058-1063
[15] 蔡利军, 朱豫才, 吕霞, 等. 模型预测控制在超超临界机组AGC协调控制和主汽温控制中的应用[J]. 中国电力, 2018, 51(7): 68-77
CAI Lijun, ZHU Yucai, LV Xia, et al. MPC applications in AGC CCS and steam temperature control on two ultrasupercritical coal-fired power generation units[J]. Electric Power, 2018, 51(7): 68-77
[16] 王婵婵. 多变量广义预测控制在锅炉燃烧系统中的应用研究[D]. 青岛: 青岛科技大学, 2015.
WANG Chanchan. Application study of multivariable generalized predictive control in boiler combustion system[D]. Qingdao: Qingdao University of Science & Technology, 2015.
[17] 高耀岿, 胡勇, 曾德良, 等. 基于磨出口温度定值优化的制粉系统多变量预测控制[J]. 动力工程学报, 2017, 37(10): 814-820
GAO Yaokui, HU Yong, ZENG Deliang, et al. Multivariable predictive control of a pulverizing system based on outlet temperature optimization of the coal mill[J]. Journal of Chinese Society of Power Engineering, 2017, 37(10): 814-820
[18] 张柯, 韦光辉, 卢佳乐, 等. 基于动态RBF网络的预测控制在中速磨煤机优化控制的应用[J]. 化工自动化及仪表, 2016, 43(1): 58-61
ZHANG Ke, WEI Guanghui, LU Jiale, et al. Application of dynamic RBF neural network-based predictive control in optimizing control over medium speed mill[J]. Control and Instruments in Chemical Industry, 2016, 43(1): 58-61
[19] 张嘉英, 席东民, 胡琳静. 广义预测控制在锅炉燃烧控制系统中的应用[J]. 热力发电, 2011, 40(2): 56-59
ZHANG Jiaying, XI Dongmin, HU Linjing. Application of the generalized predictive control in the combustion control system of boilers[J]. Thermal Power Generation, 2011, 40(2): 56-59
[20] 席裕庚. 预测控制[M]. 北京: 国防工业出版社, 1993.

基于多变量广义预测控制算法的两级过热汽温协调联动控制

Coordinative Two-Stage Superheated Steam Temperature Linkage Control Based on Multivariable Generalized Predictive Control Algorithm

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	庄家懿, 杨国华, 郑豪丰, 张鸿皓. 基于多模型融合的CNN-LSTM-XGBoost短期电力负荷预测方法[J]. 中国电力, 2021, 54(5): 46-55.
[2]	牛海明, 于佼, 丁常富, 汪朝晖, 田彬. 基于多目标粒子群算法的过热汽温自抗扰控制[J]. 中国电力, 2020, 53(3): 126-133.
[3]	黄郑, 王红星, 于海泉, 李逗, 司风琪. 基于多模型鲁棒输入训练神经网络协同的燃气-蒸汽联合循环机组传感器故障诊断方法[J]. 中国电力, 2019, 52(11): 125-133.
[4]	张晓东, 王鹏飞, 刘乐. 基于多变量广义预测控制算法的脱硝优化研究及应用[J]. 中国电力, 2019, 52(10): 178-184.
[5]	刘百成, 赵红民, 赫胜杰, 李华威, 李美双, 张继文. 单系列辅机在洛阳热电厂应用实践[J]. 中国电力, 2018, 51(5): 101-105.
[6]	王占洲, 曹丽华, 卫洪刚, 杨明. 风电介入对汽轮机转子寿命影响分析[J]. 中国电力, 2018, 51(4): 101-107.
[7]	平玉环, 管志敏, 李宗耀. 基于Smith预估的模糊自适应主汽温控制系统[J]. 中国电力, 2018, 51(11): 9-14.
[8]	杜振, 杨立强, 魏宏鸽, 叶伟平, 朱跃. 低低温电除尘器对粉尘特性和SO₃脱除效果影响分析[J]. 中国电力, 2017, 50(9): 125-128.
[9]	朱智. 贵州省“W”型火焰锅炉节能及超低排放的调研[J]. 中国电力, 2017, 50(8): 158-162.
[10]	王琦, 白建云, 王欣峰, 王力. CFB锅炉脱硫脱硝系统多目标优化[J]. 中国电力, 2017, 50(7): 109-114.
[11]	张学延, 李书伟, 刘安. 某300 MW机组高中压转子振动故障的准确诊断[J]. 中国电力, 2017, 50(5): 13-19.
[12]	钱虹, 陈丹, 杨祖魁, 郑鹏远. 约束预测控制在低氮燃烧机组过热蒸汽温度控制系统中的应用[J]. 中国电力, 2017, 50(10): 129-135.
[13]	徐君诏. 锅炉脱硝装置运行对高背压供热机组影响分析[J]. 中国电力, 2017, 50(1): 181-184.
[14]	罗嘉，陈世和，张曦，吴乐. 电站锅炉典型热工参数软测量研究[J]. 中国电力, 2016, 49(6): 48-52.
[15]	李晓枫，王亚刚. 采用IMC-PID增强超临界机组功率控制的鲁棒性[J]. 中国电力, 2016, 49(6): 20-24.

模态框（Modal）标题

基于多变量广义预测控制算法的两级过热汽温协调联动控制

Coordinative Two-Stage Superheated Steam Temperature Linkage Control Based on Multivariable Generalized Predictive Control Algorithm

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics