基于状态寻优的工业系统动态数据驱动建模
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摘要
针对应用动态数据进行系统建模的过程中,系统的初始状态无法获取的问题,提出一种基于状态寻优的工业系统动态数据驱动建模方法。上述方法选取系统运行过程历史数据中的纯动态数据作为建模数据,将建模数据末端的系统的输入值作为系统输入的稳态分量,将系统输出的稳态分量、动态数据起点的系统初始状态及模型参数均作为寻优变量的维度,应用教学优化算法进行寻优,从而建立系统的模型。对某工业系统进行建模仿真,结果表明上述方法的有效性。
In order to solve the problem of obtaining the initial state of the system in the process of dynamic historical data modeling,we proposed a dynamic data driven modeling method for industrial systems based on state optimization. The pure dynamic data from the system history data were selected as the modeling data. The value of the system input at the end of the modeling data was considered as the steady-state component of system input. The steadystate component of the system output,the initial state of the system at the starting point of the dynamic data and the model parameters were taken as the dimension of the optimization variables. Finally,applying teaching-learning-based optimization algorithm to optimize the above parameters,we establish the system model. The modeling and simulation of an industrial system show the effectiveness of the method.
In order to solve the problem of obtaining the initial state of the system in the process of dynamic historical data modeling,we proposed a dynamic data driven modeling method for industrial systems based on state optimization. The pure dynamic data from the system history data were selected as the modeling data. The value of the system input at the end of the modeling data was considered as the steady-state component of system input. The steadystate component of the system output,the initial state of the system at the starting point of the dynamic data and the model parameters were taken as the dimension of the optimization variables. Finally,applying teaching-learning-based optimization algorithm to optimize the above parameters,we establish the system model. The modeling and simulation of an industrial system show the effectiveness of the method.
引文
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[2]张金营,张秋生,韩璞,王富强.超超临界机组煤水比系统的建模与仿真研究[J].计算机仿真,2016,33(08):81-85.
[3]赵建敏,高思宇,郝萨仁高娃.换热站供热多目标优化建模研究[J].计算机仿真,2016,33(10):212-216.
[4]陈在平,程赟,贾超.基于改进子空间方法的管式加热炉系统辨识[J].计算机仿真,2016,33(2):273-276,294.
[5]窦小磊,郑玉丽.基于改进数据子空间算法的电力负荷预测问题[J].计算机仿真,2014,31(12):125-128,210.
[6]刘千,韩璞,王东风.基于ANFIS模型的烟气含氧量建模和预测[J].计算机仿真,2014,31(10):437-439,459.
[7]翟永杰,张志超.基于现场数据的SCR脱硝反应器建模[J].计算机仿真,2014,31(10):141-144,186.
[8]赵佳鹏,韩月皎,翟永杰.基于LS-SVM的再热汽温趋势预测与控制优化[J].计算机仿真,2016,3305):145-147,170.
[9]袁世通.1000MW超超临界机组建模理论与方法研究[D].华北电力大学大学(北京),2015.
[10]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.
[11]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.
[12]孙明,韩璞,程海燕,吴冠鸿.600MW燃煤机组燃烧系统的建模方法研究[J].计算机仿真,2016,33(1):416-419.
[13]高立群,欧阳海滨,孔祥勇,刘宏志.带有交叉操作的教-学优化算法[J].东北大学学报(自然科学版),2014,35(3):323-327.
[14]翁思义,杨平.自动控制原理[M].北京:中国电力出版社,2011:353-355.
[15]韩璞,等.智能控制理论及应用[M].北京:中国电力出版社,2012:6-24.
[16]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.
[17]王广军,邓良才,陈红,沈曙光.锅炉汽温对象逆动力学过程模糊辨识[J].中国电机工程学报,2007,27(20):76-80.
[18]邓良才,王广军,陈红.锅炉汽温对象的在线模糊辨识[J].中国电机工程学报,2006,26(18):111-115.
[19]王国玉,梅华,韩璞,王东风.主汽温系统模糊自适应预测函数控制[J].中国电机工程学报,2003,23(10):230-235.
[20]韩璞.现代工程控制论[M].北京:中国电力出版社,2017.