基于稳态分量估计与状态跟踪的动态数据反向建模
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摘要
针对常规工业系统建模存在的问题,提出一种基于稳态分量估计与状态跟踪的动态数据反向建模方法。该方法应用系统的动态数据,以数据末端系统输入值为输入的稳态分量并人为给定输出稳态分量,依据各稳态分量对建模数据进行处理后,将其分为三段,应用预估模型与状态观测器观测第一段数据末端的系统状态,并将其作为第二段数据对应的系统初态,第二段数据用来寻优预估模型参数及输出稳态分量,第三段数据用来验证模型。对某火电机组末级过热器进行建模,表明了该方法的有效性。
Aiming at the existing problems of conventional industrial system modeling methods, a dynamic data reserved modeling method based on steady-state component estimation and state tracking is proposed. The dynamic response data of the system is selected as the modeling data. The input value at the end of the selected data is chosen as steady-state component, and the steady-state component value of output is artificial given. Steady-state component of the modeling data is removed according to the steady-state component of input and output, and the modeling data is divided into three sections. The prediction model and the state observer are used to observe the system state at the end of the first section,and the state is regarded as system initial state corresponding to second section. The second section is used to optimize model parameters and output steady-state component. The third section of data is adopted to validate the model. The finishing superheater modeling of a thermal power unit is carried out,and the simulation results show the method effectiveness.
Aiming at the existing problems of conventional industrial system modeling methods, a dynamic data reserved modeling method based on steady-state component estimation and state tracking is proposed. The dynamic response data of the system is selected as the modeling data. The input value at the end of the selected data is chosen as steady-state component, and the steady-state component value of output is artificial given. Steady-state component of the modeling data is removed according to the steady-state component of input and output, and the modeling data is divided into three sections. The prediction model and the state observer are used to observe the system state at the end of the first section,and the state is regarded as system initial state corresponding to second section. The second section is used to optimize model parameters and output steady-state component. The third section of data is adopted to validate the model. The finishing superheater modeling of a thermal power unit is carried out,and the simulation results show the method effectiveness.
引文
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[21]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.
[22]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.
[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.
[24]王万召,王杰.采用限定记忆极限学习机的过热汽温逆建模研究[J].西安交通大学学报,2014,48(2):32-37.Wang Wanzhao,Wang Jie.Inverse Modeling for Superheated Steam Temperature Based on Restricted Memory Extreme Learning Machine[J].Journal of Xi’an Jiaotong University,2014,48(2):32-37.
[2]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.Maximum likelihood identification method for a multivariable controlled autoregressive moving average system[J].Journal of Shandong University(Engineering Science),2015,45(2):49-55,74.
[3]魏彤,田双彪.基于RLS-DE算法的多变量径向磁轴承系统辨识[J].机械工程学报,2016,52(3):143-150.Wei Tong,Tian Shuangbiao.The Identification of Multivariable Radial Magnetic Bearing System Based on RLS-DE Algorithm[J].Journal of Mechanical Engineering,2016,52(3):143-150.
[4]Verhaegen M,Dewilde P.Subspace model identification.Part 1.The output-error state-space model identification class of algorithms[J].International Journal of Control(S0020-7179),1992,56(5):1187-1210.
[5]Verhaegen M,Dewilde P.Subspace model identification.Part2.Analysis of the elementary output-error state-space model identification algorithm[J].International Journal of Control(S0020-7179),1992,56(5):1211-1241.
[6]Verhaegen M.Subspace model identification.Part 3.Analysis of the ordinary output-error state-space model identification algorithm[J].International Journal of Control(S0020-7179),1993,58(3):555-586.
[7]Qin S J.An overview of subspace identification[J].Computers and Chemical Engineering(S0098-1354),2006,30(10/11/12):1502-1513.
[8]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.
[9]J Y Huang,G L Ji,Y C Zhu,et al.Identification of multi-model LPV models with two scheduling variables[J].Journal of Process Control(S0959-1524),2012,22(7):1198-1208.
[10]Y C Zhu,P Rohit,W B Stephen,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.
[11]侯杰,刘涛.基于新息估计和正交投影的闭环子空间模型辨识[J].自动化学报,2016,42(11):1657-1663.Hou Jie,Liu Tao.Closed-loop Subspace Model Identification Using Innovation Estimation and Orthogonal Projection[J].Acta Automatica Sinica,2016,42(11):1657-1663.
[12]付忠广,靳涛,周丽君,等.复杂系统反向建模方法及偏最小二乘法建模应用研究[J].中国电机工程学报,2009,29(2):25-29.Fu Zhongguang,Jin Tao,Zhou Lijun,et al.Research and Application of the Reversed Modeling Method and Partial Least-square Regression Modeling for the Complex Thermal System[J].Proceedings of the CSEE,2009,29(2):25-29.
[13]X J Liu,X B Kong,G L Hou,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.
[14]曲亚鑫,曾水平,万常翔.基于能量平衡原理的锅炉再热汽温反向建模[J].仪器仪表学报,2013,34(1):38-44.Qu Yaxin,Zeng Shuiping,Wan Changxiang.Reversed modeling of boiler reheat steam temperature based on energy balance[J].Chinese Journal of Scientific Instrument,2013,34(1):38-44.
[15]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.
[16]肖峰,陈国初.基于改进果蝇算法优化的SVM风电功率短期预测[J].华东理工大学学报(自然科学版),2016,42(3):420-426.Xiao Feng,Chen Guochu.Wind Power Short-Term Prediction Based on SVM Trained by Improved FOA[J].Journal of East China University of Science and Technology(Natural Science Edition),2016,42(3):420-426.
[17]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.
[18]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 of 300MW CFB boiler’s combustion system[D].Beijing:North China Electric Power University,2010.
[19]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.
[20]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 1 000 MW ultra supercritical unit[D].Beijing:North China Electric Power University,2015.
[21]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.
[22]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.
[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.
[24]王万召,王杰.采用限定记忆极限学习机的过热汽温逆建模研究[J].西安交通大学学报,2014,48(2):32-37.Wang Wanzhao,Wang Jie.Inverse Modeling for Superheated Steam Temperature Based on Restricted Memory Extreme Learning Machine[J].Journal of Xi’an Jiaotong University,2014,48(2):32-37.