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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

王懋譞, 王永富, 柴天佑, 张晓宇. 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. 自动化学报, 2020, 45(x): 1−16 doi: 10.16383/j.aas.c200195
引用本文: 王懋譞, 王永富, 柴天佑, 张晓宇. 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. 自动化学报, 2020, 45(x): 1−16 doi: 10.16383/j.aas.c200195
Wang Mao-Xuan, Wang Yong-Fu, Chai Tian-You, Zhang Xiao-Yu. External Generalized Predictive Cascade Control for Main Steam Temperature Based on Weight Factor Self-Regulating. Acta Automatica Sinica, 2020, 45(x): 1−16 doi: 10.16383/j.aas.c200195
Citation: Wang Mao-Xuan, Wang Yong-Fu, Chai Tian-You, Zhang Xiao-Yu. External Generalized Predictive Cascade Control for Main Steam Temperature Based on Weight Factor Self-Regulating. Acta Automatica Sinica, 2020, 45(x): 1−16 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
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: CHAI 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

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

Funds: Supported by National Natural Science Foundation of China(51775103)
  • 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-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型模糊神经网络(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(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.
  • 图  1  主蒸汽温度串级PI控制系统

    Fig.  1  Cascade PI control system of main steam temperature

    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

    图  3  T-S型FNN模型结构

    Fig.  3  Structure of T-S FNN model

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

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

    图  5  权重因子 $\lambda_{k}$ 模糊自校正曲面

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

    图  6  工况1仿真结果

    Fig.  6  The simulation results in case 1

    图  7  工况2仿真结果

    Fig.  7  The simulation results in case 2

    图  8  优化目标函数仿真结果

    Fig.  8  The simulation results of optimization objective function

    图  9  外挂广义预测串级控制架构

    Fig.  9  Diagram of external generalized predictive cascade control

    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

    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

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