基于大数据和神经网络的锅炉燃烧含氧量建模研究
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摘要
提出一种基于大数据的神经网络辨识多输入单输出(Multiple-Input Single-Output,MISO)系统的方法,采集现场运行的锅炉燃烧系统总风量、总煤量、炉膛氧量等历史大数据,首先使用数据平滑、去除趋势性、归一化等步骤进行数据预处理,然后利用近邻法删选出表征系统特性的样本数据集,利用神经网络模型进行训练后挖掘出数据之间的关系,最后在升、降30%负荷的情况下分别进行模型预测。结果表明,虽然只将采集到约0. 658%数据容量进行训练,但在对整个大数据容量进行测试时,模型误差仍在允许的范围内。
A method of neural network identification MISO system based on large data is presented in this paper. After collecting the data of the total air volume,total coal and oxygen in the boiler combustion system. the data preprocessing is performed as data smoothing,trend removing and normalization. Then,the nearest neighbor method is used to delete the sample data set which can represent the system characteristics. At last,the model is predicted in the cases of increasing and decreasing 30% load. The results show that although only about 0. 658% of the collected data capacity is trained,the model error is still within the allowable range when testing the entire large data capacity.
A method of neural network identification MISO system based on large data is presented in this paper. After collecting the data of the total air volume,total coal and oxygen in the boiler combustion system. the data preprocessing is performed as data smoothing,trend removing and normalization. Then,the nearest neighbor method is used to delete the sample data set which can represent the system characteristics. At last,the model is predicted in the cases of increasing and decreasing 30% load. The results show that although only about 0. 658% of the collected data capacity is trained,the model error is still within the allowable range when testing the entire large data capacity.
引文
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[4]张小桃,倪维斗,李政,等.基于现场数据与神经网络的热工对象动态建模[J].热能动力工程,2005,20(1):34-39.ZHANG Xiao-tao,NI Wei-dou,LI Zheng,et al.Dynamic modeling of thermal object based on field data and neural network[J].Journal of Engineering for Thermal Energy and Power,2005,20(1):34-39.
[5]王新超,钱烽雷.基于数据驱动的模糊系统辨识研究[J].系统仿真技术,2016,12(3):223-227.WANG Xin-chao,QIAN Feng-lei.Research on fuzzy system identification using data drive[J]System Simulation Technology,2016,12(3):223-227.
[6]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.YUAN Shi-tong,HAN Pu,SUN Ming.Modeling research of multivariable system based on big data[J].Journal of System Simulation,2014,26(7):1454-1459.
[7]牛玉广,沙超,康俊杰.基于数值模拟与试验运行数据的电站锅炉燃烧系统复合建模[J].动力工程学报,2014,34(10):765-770.NIU Yu-guang,SHA Chao,KANG Jun-jie.Hybrid modeling of boiler combustion system based on numerical simulation and experimental data[J].Journal of Chinese Society of Power Engineering,2014,34(10):765-770.
[8]张健美,周步祥,林楠,等.灰色Elman神经网络的电网中长期负荷预测[J].电力系统及其自动化学报,2013,25(4):145-149.ZHANG Jian-mei,ZHOU Bu-xiang,LIN Nan,et al.Prediction of mid-long term load based on gray elman neural networks[J].Proceedings of the CSU-EPSA,2013,25(4):145-149.
[9]郝红卫,蒋蓉蓉.基于最近邻规则的神经网络训练样本选择方法[J].自动化学报,2007,33(12):1247-1251.HAO Hong-wei,JIANG Rong-rong.Training sample selection method for neural networks based on nearest neighbor rule[J].Acta Automatica Sinica,2007,33(12):1247-1251.
[10]史会峰,牛东晓,卢艳霞.基于贝叶斯神经网络短期负荷预测模型[J].中国管理科学,2012,20(4):118-124.SHI Hui-feng,NIU Dong-xiao,LU Yan-xia.The short-term load forecasting model based on Bayesian neural network[J].Chinese Journal of Management Science,2012,20(4):118-124.