基于状态跟踪的非线性工业系统全工况建模
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摘要
提出一种基于状态跟踪的非线性工业系统全工况建模方法。针对历史数据量过大,建模数据筛选困难的问题,设计一滑动窗口筛选稳态数据,推导了窗口中标准差的快速递推算法;分析未知扰动对系统的影响机制,选取由动态回归稳态的数据建模,提出一种可消除扰动影响的数据驱动建模算法;利用过程工业大数据包含的模型信息,应用高次函数拟合各工况模型参数,提出一种基于特征参数的线性变参数传递函数模型。对某工业过程进行辨识,表明了有效性。
From the prospective of industrial big data modeling, this paper presents a modeling method for nonlinear industrial system at all operating conditions based on state tracking. In view of large amount of historical data and the difficulty to screen the modeling data, a sliding window is designed to screen steady-state data. The fast calculation method for the standard deviation is deducted. The influence mechanism of unknown disturbance on the system is analyzed. The data segment, representing the system from dynamic state to stable state, is selected as the modeling data. A data-driven modeling algorithm, which can effectively eliminate the disturbance influence, is proposed. The model information contained in the process industry big data is adopted and the high order function is applied to fit the model parameters. A linear transfer function model with variable parameter based on the characteristic parameters is proposed. The effectiveness of the proposed method is verified by modeling an industrial process.
From the prospective of industrial big data modeling, this paper presents a modeling method for nonlinear industrial system at all operating conditions based on state tracking. In view of large amount of historical data and the difficulty to screen the modeling data, a sliding window is designed to screen steady-state data. The fast calculation method for the standard deviation is deducted. The influence mechanism of unknown disturbance on the system is analyzed. The data segment, representing the system from dynamic state to stable state, is selected as the modeling data. A data-driven modeling algorithm, which can effectively eliminate the disturbance influence, is proposed. The model information contained in the process industry big data is adopted and the high order function is applied to fit the model parameters. A linear transfer function model with variable parameter based on the characteristic parameters is proposed. The effectiveness of the proposed method is verified by modeling an industrial process.
引文
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[20]张小桃,倪维斗,李政,等.基于主元分析与现场数据的过热汽温动态建模研究[J].中国电机工程学报,2005,25(5):131-135.Zhang Xiaotao,Ni Weidou,Li Zheng,et al.Dynamic modeling study of superheater steam temperature based on principal component analysis method and online data[J].Proceedings of the CSEE,2005,25(5):131-135.
[21]黄宇,韩璞,李永玲.主汽温系统模糊自适应内模控制[J].中国电机工程学报,2008,28(23):93-98.Huang Yu,Han Pu,Li Yongling.Fuzzy Adaptive Internal Model Control in Main Steam Temperature System[J].Proceedings of the CSEE,2008,28(23):93-98.
[22]于希宁,李亮,范瑾.改进遗传算法在CFB锅炉热工过程建模中的应用[J].系统仿真学报,2008,20(17):4727-4730,4736.Yu Xining,Li Liang,Fan Jin.Application of Improved Genetic Algorithm to Circulating Fluidized Bed Boiler’s Thermal Processes Modeling[J].Journal of System Simulation,2008,20(17):4727-4730,4736.
[23]翁思义,杨平.自动控制原理[M].北京:中国电力出版社,2011.Weng Siyi,Yang Ping.Automatic Control Theory[M].Beijing:China Electric Power Press,2011.
[2]鲁兴举,郑志强.一类MIMO系统连续状态空间模型的参数辨识频域方法[J].自动化学报,2016,42(1):145-153.Lu Xingju,Zheng Zhiqiang.Identification of Continuous State-space Model Parameters for a Class of MIMO Systems:A Frequency Domain Approach[J].Acta Automatica Sinica,2016,42(1):145-153.
[3]高艳普,王向东,王冬青.多变量受控自回归滑动平均系统的极大似然辨识方法[J].山东大学学报(工学版),2015,45(2):49-55,74.Gao Yanpu,Wang Xiangdong,Wang Dongqing.M aximum likelihood identification method for a multivariable controlled autoregressive moving average system[J].Journal of Shandong University(Engineering Science),2015,45(2):49-55,74.
[4]Huang J Y,Ji G L,Zhu Y C,et al.Identification of multi-model LPV models with two scheduling variables[J].Journal of Process Control(S0959-1524),2012,22(7):1198-1208.
[5]Zhu Y C,Rohit P,Stephen W B,et al.Toward a low cost and high performance MPC:The role of system identification[J].Computers&Chemical Engineering(S0098-1354),2013,51:124-135.
[6]王新,吕剑虹,向文国.一种改进的子空间辨识方法在热工过程中的应用[J].中国电机工程学报,2010,30(32):96-102.Wang Xin,LüJianhong,Xiang Wenguo.A Modified Subspace Identification Method and Its Application in Thermal Process[J].Proceedings of the CSEE,2010,30(32):96-102.
[7]娄海川,苏宏业,古勇,等.基于修正闭环子空间辨识–分段线性结构的环管式丙烯聚合反应过程非线性模型预测控制[J].控制理论与应用,2015,32(8):1040-1051.Lou Haichuan,Su Hongye,Gu Yong,et al.Nonlinear predictive control with modified closed-loop subspace identification-piecewise linear model for double-loop propylene polymerization process[J].Control Theory&Applications,2015,32(8):1040-1051.
[8]Wu X D,Zhu X Q,Wu G Q,et al.Data mining with big data[J].IEEE Transactions on Knowledge and Data Engineering(S1041-4347),2014,26(1):97-107.
[9]刘强,秦泗钊.过程工业大数据建模研究展望[J].自动化学报,2016,42(2):161-171.Liu Qiang,Qin Sizhao.Perspectives on Big Data Modeling of Process Industries[J].Acta Automatica Sinica,2016,42(2):161-171.
[10]仇晓智,徐治皋,张林萌,等.基于扩展最小资源分配网络的热工过程辨识[J].动力工程学报,2009,29(5):432-435.Qiu Xiaozhi,Xu Zhigao,Zhang Lingmeng,et al.Identification of Thermal Process Based on Extended Minimal Resource Allocation Network[J].Journal of Chinese Society of Power Engineering,2009,29(5):432-435.
[11]牛培峰,肖兴军,李国强,等.基于万有引力搜索算法的电厂锅炉NO_x排放模型的参数优化[J].动力工程学报,2013,33(2):100-106.Niu Peifeng,Xiao Xingjun,Li Guoqiang,et al.Parameter Optimization for NOx Emission Model of Power Plant Boilers Based on Gravitational Search Algorithm[J].Journal of Chinese Society of Power Engineering,2013,33(2):100-106.
[12]Liu X J,Kong X B,Hou G L,et al.Modeling of a 1000MW power plant ultra super-critical boiler system using fuzzy-neural network methods[J].Energy Conversion and Management(S0196-8904),2013,65:518-527.
[13]刘吉臻,秦天牧,杨婷婷,等.基于偏互信息的变量选择方法及其在火电厂SCR系统建模中的应用[J].中国电机工程学报,2016,36(9):2438-2443.Liu Jizhen,Qin Tianmu,Yang Tingting,et al.Variable Selection Method Based on Partial Mutual Information and Its Application in Power Plant SCR System Modeling[J].Proceedings of the CSEE,2016,36(9):2438-2443.
[14]韩璞,董泽,王东风,等.智能控制理论及应用[M].北京:中国电力出版社,2012.Han Pu,Dong Ze,Wang Dongfeng,et al.Intelligent Control Theory and Application[M].Beijing:China Electric Power Press,2012.
[15]孙剑.大型循环流化床锅炉燃烧系统特性与建模研究[D].北京:华北电力大学大学,2010.Sun Jian.Characteristic and Modeling Research of300MW CFB Boiler's Combustion System[D].Beijing:North China Electric Power University,2010.
[16]袁世通.1 000 MW超超临界机组建模理论与方法研究[D].北京:华北电力大学大学,2015.Yuan Shitong.Research on Modeling Theory and method for 1000MW ultra supercritical unit[D].Beijing:North China Electric Power University,2015.
[17]袁世通,韩璞,孙明.基于大数据的多变量系统建模方法研究[J].系统仿真学报,2014,26(7):1454-1459,1510.Yuan Shitong,Han Pu,Sun Ming.Modeling Research of Multivariable System Based on Big Data[J].Journal of System Simulation,2014,26(7):1454-1459,1510.
[18]韩璞,袁世通.基于大数据和双量子粒子群算法的多变量系统辨识[J].中国电机工程学报,2014,34(32):5779-5787.Han Pu,Yuan Shitong.Multivariable system identification based on double quantum particle swarm optimization and big data[J].Proceedings of the CSEE,2014,34(32):5779-5787.
[19]方崇智,萧德云.过程辨识[M].北京:清华大学出版社,2007:18-19.Fang Chongzhi,Xiao Deyun.Process Identification[M].Beijing:Tsinghua University Press,2007:18-19.
[20]张小桃,倪维斗,李政,等.基于主元分析与现场数据的过热汽温动态建模研究[J].中国电机工程学报,2005,25(5):131-135.Zhang Xiaotao,Ni Weidou,Li Zheng,et al.Dynamic modeling study of superheater steam temperature based on principal component analysis method and online data[J].Proceedings of the CSEE,2005,25(5):131-135.
[21]黄宇,韩璞,李永玲.主汽温系统模糊自适应内模控制[J].中国电机工程学报,2008,28(23):93-98.Huang Yu,Han Pu,Li Yongling.Fuzzy Adaptive Internal Model Control in Main Steam Temperature System[J].Proceedings of the CSEE,2008,28(23):93-98.
[22]于希宁,李亮,范瑾.改进遗传算法在CFB锅炉热工过程建模中的应用[J].系统仿真学报,2008,20(17):4727-4730,4736.Yu Xining,Li Liang,Fan Jin.Application of Improved Genetic Algorithm to Circulating Fluidized Bed Boiler’s Thermal Processes Modeling[J].Journal of System Simulation,2008,20(17):4727-4730,4736.
[23]翁思义,杨平.自动控制原理[M].北京:中国电力出版社,2011.Weng Siyi,Yang Ping.Automatic Control Theory[M].Beijing:China Electric Power Press,2011.