基于权重因子自校正的主蒸汽温度外挂广义预测串级控制

王懋譞; 王永富; 柴天佑; 张晓宇

doi:10.16383/j.aas.c200195

基于权重因子自校正的主蒸汽温度外挂广义预测串级控制

doi: 10.16383/j.aas.c200195

1.
东北大学机械工程与自动化学院沈阳 110819
2.
东北大学流程工业综合自动化国家重点实验室沈阳 110819
3.
国家能源投资集团北京 100011

基金项目: 国家自然科学基金(51775103)资助

详细信息

作者简介:
王懋譞：东北大学机械工程与自动化学院博士研究生. 主要研究方向为模型预测控制及其在电厂中的应用. E-mail: wangmx2238@163.com

王永富：东北大学机械工程与自动化学院教授. 1998年获得东北大学机械电子专业硕士学位, 2005年获得东北大学控制理论与控制工程专业博士学位. 主要研究方向为机电系统模糊建模与控制, 新能源汽车, 电厂的智能优化控制. 本文通信作者. E-mail: yfwang@mail.neu.edu.cn

柴天佑：中国工程院院士, 东北大学教授, IEEE Fellow, IFAC Fellow. 1985年获得东北大学博士学位. 主要研究方向为自适应控制, 智能解耦控制, 流程工业综台自动化理论、方法与技术.E-mail: tychai@mail.neu.edu.cn

张晓宇：国家能源投资集团工程师. 2014年获得清华大学博士学位. 主要研究方向为大型电厂锅炉的燃烧优化控制. E-mail: 16810116@shenhua.cc

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出版历程
- 收稿日期: 2020-04-08
- 录用日期: 2020-07-12
- 网络出版日期: 2021-12-13
- 刊出日期: 2022-02-18

External Generalized Predictive Cascade Control for Main Steam Temperature Based on Weight Factor Self-regulating

1.
School of Mechanical Engineering and Automation, Northeastern University, Shenyang, 110819
2.
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819
3.
China Energy Investment Corporation Limited, Beijing, 100011

Funds: Supported by National Natural Science Foundation of China (51775103)

More Information

Author Bio:
WANG Mao-Xuan　Ph.D. candidate at the School of Mechanical Engineering and Automation, Northeastern University. His research interest covers model predictive control and its application in power plants

WANG Yong-Fu　Professor at the School of Mechanical Engineering and Automation, Northeastern University. He received his master degree in mechanical engineering and Ph.D. degree in control theory and control engineering from Northeastern University in 1998 and 2005, respectively. His research interest covers fuzzy modeling and intelligent control of mechanical engineering, new energy vehicles, and intelligent optimal control in power plants. Corresponding author of this paper

CHAI Tian-You　Academician of Chinese Academy of Engineering, professor at Northeastern University, IEEE Fellow, IFAC Fellow. He received his Ph.D. degree from Northeastern University in 1985. His research interest covers adaptive control, intelligent decoupling control, and integrated automation theory, method and technology of industrial process

ZHANG Xiao-Yu　Engineer at China Energy Investment Corporation Limited. He received his Ph.D. degree from Tsinghua University in 2014. His research interest covers optimized combustion control of large power plant boilers

摘要

摘要: 针对电厂目前普遍采用PI-PI串级控制器调节锅炉主蒸汽温度系统, 不能有效克服惯性、时滞和参数时变等问题的影响, 本文提出了一种理想GPC (Generalized predictive control)-PI串级控制器. 首先, 该理想串级控制器不仅能抑制一次和二次扰动, 而且外环GPC通过对主蒸汽温度的多步预测, 并结合滚动优化技术能有效克服主蒸汽温度系统的惯性和时滞问题. 另外, 针对主蒸汽温度系统参数时变的特性, 该理想控制器采用了T-S (Takagi-Sugeno)型模糊神经网络(Fuzzy neural network, FNN)作为主蒸汽温度模型, 该模型能够通过反馈校正技术实时更新模型参数. 同时, 为了改善主蒸汽温度系统动态响应品质和稳定性, 对外环GPC中的权重因子进行了模糊自校正设计, 通过理论分析和对比仿真验证了该理想GPC-PI串级控制器优于权重因子固定的GPC-PI和PI-PI串级控制器. 最后, 考虑到直接将电厂集散控制系统(Distributed control system, DCS)中的PI-PI串级控制器升级为理想GPC-PI串级控制器存在安全以及风险责任等问题, 故将电厂的传统PI-PI串级控制器升级成外挂的GPC-PI-PI串级控制器, 既改善了锅炉主蒸汽温度的控制效果又规避了风险责任, 实际应用验证了该方法的有效性.
- 主蒸汽温度 /
- 广义预测控制 /
- 模糊神经网络 /
- 权重因子 /
- 自校正 /
- 串级控制
Abstract: The PI-PI cascade controller, which is widely used in power plants for adjusting main steam temperature, cannot effectively overcome the negative effects caused by inertia, time delay and time-varying parameters, therefore an ideal generalized predictive control (GPC)-PI cascade controller is proposed in this paper. Firstly, the GPC-PI cascade controller could attenuate the primary and secondary disturbances. To solve the problems of inertia and time delay in the main steam temperature system, the GPC-PI cascade controller predicts the multi-step main steam temperature integrating with rolling optimization technique. In addition, facing the time-varying parameters of the main steam temperature system, the GPC-PI cascade controller adopts the T-S (Takagi-Sugeno) fuzzy neural networks (FNN) as the main steam temperature model, whose parameters can be identified and updated in real time. Meanwhile, in order to further improve the dynamic response speed and stability of the main steam temperature system, fuzzy self-regulating of weight factor in outer-loop GPC is designed. The theoretical analysis and comparative simulations verify that the ideal GPC-PI cascade controller is superior to the GPC-PI cascade controller with fixed weight factor and the PI-PI cascade controller. Finally, considering the safety and risks due to the substitution of PI-PI cascade controller by the ideal GPC-PI cascade controller in distributed control systems (DCS), the PI-PI cascade controller is upgraded to the GPC-PI-PI controller for power plant, which not only improves the control effects but also avoids liabilities for risks. The practical application demonstrates the effectiveness of the GPC-PI-PI controller.
- Main steam temperature /
- generalized predictive control (GPC) /
- fuzzy neural network (FNN) /
- weight factor /
- self-regulating /
- cascade control
注释:

1) 收稿日期 2020-04-08 录用日期 2020-07-12 Manuscript received April 8, 2020; accepted July 12, 2020 国家自然科学基金 (51775103) 资助 Supported by National Natural Science Foundation of China (51775103) 本文责任编委乔俊飞 Recommended by Associate Editor QIAO Jun-Fei

2) 1. 东北大学机械工程与自动化学院沈阳 110819 2. 东北大学流程工业综合自动化国家重点实验室沈阳 110819 3. 国家能源投资集团北京 100011 1. School of Mechanical Engineering and Automation, North-eastern University, Shenyang 110819 2. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819 3. China Energy Investment Corporation Limited, Beijing 100011

HTML全文

图 1 主蒸汽温度串级PI控制系统

Fig. 1 Cascade PI control system of main steam temperature

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图 2 理想广义预测串级控制系统结构

Fig. 2 Diagram of ideal generalized predictive cascade control system

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图 3 T-S型FNN模型结构

Fig. 3 Structure of T-S FNN model

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图 4 $\hat{e},$ $\Delta\hat{e}$和$\lambda_{k}$隶属度函数

Fig. 4 The membership function of $\hat{e} ,$$\Delta\hat{e},$ and $\lambda_{k}$

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图 5 权重因子$\lambda_{k}$模糊自校正曲面

Fig. 5 Fuzzy self-regulating surface of weight factor $\lambda_{k}$

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图 6 工况1仿真结果

Fig. 6 The simulation results in Case 1

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图 7 工况2仿真结果

Fig. 7 The simulation results in Case 2

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图 8 优化目标函数仿真结果

Fig. 8 The simulation results of optimization objective function

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图 9 外挂广义预测串级控制架构

Fig. 9 Diagram of external generalized predictive cascade control

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图 10 外挂广义预测串级控制系统等效图

Fig. 10 Equivalent diagram of external generalized predictive cascade control system

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图 11 不同负荷下主蒸汽温度对比控制效果

Fig. 11 The comparison of main steam temperature control effect under different loads

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表 1 权重因子$ \lambda_{k} $模糊调节规则

Table 1 Fuzzy regulation rules of weight factor $ \lambda_{k} $

$ \Delta\hat{e} $	$ \hat{e} $
$ \Delta\hat{e} $	NB	NS	ZE	PS	PB
NB	NL	NB	NM	NB	NL
NS	NS	ZE	PS	ZE	NS
ZE	PM	PB	PL	PB	PM
PS	NS	ZE	PS	ZE	NS
PB	NL	NB	NM	NB	NL

下载: 导出CSV

表 2 实验结果性能比较

Table 2 Performance comparison of experimental results

负荷	控制器	指标
负荷	控制器	$ \epsilon $	RMSE	MAE	IAE
600 MW	原始	0.9319	0.3677	0.3012	108.7194
600 MW	外挂	0.7954	0.3372	0.2668	96.3206
480 MW	原始	1.3560	0.4593	0.3635	131.2284
480 MW	外挂	0.6856	0.2516	0.2011	72.5914
310 MW	原始	0.9791	0.3015	0.2230	80.5173
310 MW	外挂	0.7458	0.2789	0.2222	80.2247

下载: 导出CSV

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