基于相关向量机的热工参数软测量
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摘要
针对火电机组中某些重要热工参数难以直接测量的问题,本文提出了一种基于相关向量机(RVM)的热工参数软测量方法。采用组合核函数的形式代替传统的单一核函数,采用改进的鲸鱼优化算法(IWOA)优化核函数参数和组合核权重系数,利用优化后的参数和机组实际工况数据建立IWOA-RVM软测量模型,并采用样本更新策略实现该模型的在线更新。将该模型应用于某600 MW燃煤机组锅炉参数测量,结果表明:本文提出的IWOA-RVM软测量模型的烟气含氧量和飞灰含碳量预测结果均方根误差分别为0.085 2和0.088 4,均低于BP神经网络和支持向量机软测量模型;本文模型所需的相关向量少,模型稀疏性好,测试精度高,具有较强的泛化能力。
Some important thermal parameters of thermal power units are difficult to measure directly, to solve this problem, a soft-measurement method based on correlation vector machine(RVM) was proposed. In this method,the combined kernel function replaces the conventional single kernel function, the improved whale optimization algorithm(IWOA) is used to optimize the kernel function parameters and the combined kernel weight coefficient,and the IWOA-RVM soft measurement model is established by using the optimized parameters and the actual operating conditions data of the unit. Moreover, the sample update strategy is applied to realize online update of the model. Furthermore, the above model was employed to the parameter measurement of a 600 MW coal-fired boiler.The results show that, by using the IWOA-RVM soft measurement model, the root mean square error of the predicted oxygen content in flue gas and carbon content in fly ash is 0.085 2 and 0.088 4, both are lower than that by the BP neural network and support vector machine soft measurement model. The proposed model requires less correlation vector, and has good model sparseness, high test accuracy and strong generalization ability.
Some important thermal parameters of thermal power units are difficult to measure directly, to solve this problem, a soft-measurement method based on correlation vector machine(RVM) was proposed. In this method,the combined kernel function replaces the conventional single kernel function, the improved whale optimization algorithm(IWOA) is used to optimize the kernel function parameters and the combined kernel weight coefficient,and the IWOA-RVM soft measurement model is established by using the optimized parameters and the actual operating conditions data of the unit. Moreover, the sample update strategy is applied to realize online update of the model. Furthermore, the above model was employed to the parameter measurement of a 600 MW coal-fired boiler.The results show that, by using the IWOA-RVM soft measurement model, the root mean square error of the predicted oxygen content in flue gas and carbon content in fly ash is 0.085 2 and 0.088 4, both are lower than that by the BP neural network and support vector machine soft measurement model. The proposed model requires less correlation vector, and has good model sparseness, high test accuracy and strong generalization ability.
引文
[1]罗嘉,吴乐.电站锅炉主要热工过程参数软测量技术研究进展[J].热力发电,2015,44(11):1-9.LUO Jia,WU Le.Research status of soft measurement technology of typical thermal parameters for utility boilers[J].Thermal Power Generation,2015,44(11):1-9.
[2]赵宇红,张韵辉.软计算在钢球磨煤机制粉系统中的应用研究[J].热力发电,2011,40(12):17-21.ZHAO Yuhong,ZHANG Yunhui.Study on application of soft-computation to ball mill coal-pulverizing system[J].Thermal Power Generation,2011,40(12):17-21.
[3]俞金寿.软测量技术及其应用[J].自动化仪表,2008(1):1-7.YU Jinshou.Soft sensing technology and its application[J].Process Automation Instrumentation,2008(1):1-7.
[4]湛腾西,郭观七.电厂烟气含氧量的智能混合预测方法[J].仪器仪表学报,2010,31(8):1826-1833.ZHAN Tengxi,GUO Guanqi.Intelligent hybrid prediction method of the flue gas oxygen content in power plant[J].Chinese Journal of Scientific Instrument,2010,31(8):1826-1833.
[5]王培红,李磊磊,陈强,等.人工智能技术在电站锅炉燃烧优化中的应用研究[J].中国电机工程学报,2004(4):184-188.WANG Peihong,LI Leilei,CHEN Qiang,et al.Research on applications of artificial intelligence to combustion optimization in a coal-fired boiler[J].Proceedings of the CSEE,2004(4):184-188.
[6]颜晓娟,龚仁喜,张千锋.优化遗传算法寻优的SVM在短期风速预测中的应用[J].电力系统保护与控制,2016,44(9):38-42.YAN Xiaojuan,GONG Renxi,ZHANG Qianfeng.Application of optimization SVM based on improved genetic algorithm in short-term wind speed prediction[J].Power System Protection and Control,2016,44(9):38-42.
[7]卞和营,方彦军.基于支持向量回归的飞灰含碳量软测量[J].热力发电,2014,43(10):46-50.BIAN Heying,FANG Yanjun.SVR-based study on softsensing measurement of carbon content in fly ash[J].Thermal Power Generation,2014,43(10):46-50.
[8]罗嘉,陈世和,张曦,等.电站锅炉典型热工参数软测量研究[J].中国电力,2016,49(6):48-52.LUO Jia,CHEN Shihe,ZHANG Xi,et al.Research on soft measurement technology of typical thermal parameters of utility boilers[J].Electric Power,2016,49(6):48-52.
[9]TIPPING M E,SMOLA A.Sparse Bayesian learning and the relevance vector machine.[J].Journal of Machine Learning Research,2001,1(3):211-244.
[10]张亚男,杨慧中.基于RVM组合核优化的软测量模型研究[J].系统仿真学报,2018(1):272-277.ZHANG Yanan,YANG Huizhong.RVM soft sensing model based on optimized combined kernel function[J].Journal of System Simulation,2018(1):272-277.
[11]PSORAKIS I,DAMOULAS T,GIROLAMI M A.Multiclass relevance vector machines:an evaluation of sparsity and accuracy[J].IEEE Transactions on Neural Networks,2010,21(10):1588-1598.
[12]MIRJALILI S,LEWIS A.The whale optimization algorithm[J].Advances in Engineering Software,2016,95:51-67.
[13]蔡丹,季晓勇,史贺,等.改进分段Logistic混沌映射的方法及其性能分析[J].南京大学学报(自然科学版),2016(5):809-815.CAI Dan,JI Xiaoyong,SHI He,et al.Method for improving piecewise Logistic chaotic map and its performance analysis[J].Journal of Nanjing University(Natural Sciences),2016(5):809-815.
[14]李健,张彬文.烟气含氧量软测量方法研究[J].自动化仪表,2016(6):83-85.LI Jian,ZHANG Binwen.Research on the soft sensing methods of oxygen content in flue gas[J].Process Automation Instrumentation,2016(6):83-85.
[15]牛玉广,彭范.飞灰含碳量微波频率响应特性研究[J].仪器仪表学报,2017,38(11):2634-2642.NIU Yuguang,PENG Fan.Microwave frequency response characteristic of fly ash with different carbon content[J].Chinese Journal of Scientific Instrument,2017,38(11):2634-2642.
[2]赵宇红,张韵辉.软计算在钢球磨煤机制粉系统中的应用研究[J].热力发电,2011,40(12):17-21.ZHAO Yuhong,ZHANG Yunhui.Study on application of soft-computation to ball mill coal-pulverizing system[J].Thermal Power Generation,2011,40(12):17-21.
[3]俞金寿.软测量技术及其应用[J].自动化仪表,2008(1):1-7.YU Jinshou.Soft sensing technology and its application[J].Process Automation Instrumentation,2008(1):1-7.
[4]湛腾西,郭观七.电厂烟气含氧量的智能混合预测方法[J].仪器仪表学报,2010,31(8):1826-1833.ZHAN Tengxi,GUO Guanqi.Intelligent hybrid prediction method of the flue gas oxygen content in power plant[J].Chinese Journal of Scientific Instrument,2010,31(8):1826-1833.
[5]王培红,李磊磊,陈强,等.人工智能技术在电站锅炉燃烧优化中的应用研究[J].中国电机工程学报,2004(4):184-188.WANG Peihong,LI Leilei,CHEN Qiang,et al.Research on applications of artificial intelligence to combustion optimization in a coal-fired boiler[J].Proceedings of the CSEE,2004(4):184-188.
[6]颜晓娟,龚仁喜,张千锋.优化遗传算法寻优的SVM在短期风速预测中的应用[J].电力系统保护与控制,2016,44(9):38-42.YAN Xiaojuan,GONG Renxi,ZHANG Qianfeng.Application of optimization SVM based on improved genetic algorithm in short-term wind speed prediction[J].Power System Protection and Control,2016,44(9):38-42.
[7]卞和营,方彦军.基于支持向量回归的飞灰含碳量软测量[J].热力发电,2014,43(10):46-50.BIAN Heying,FANG Yanjun.SVR-based study on softsensing measurement of carbon content in fly ash[J].Thermal Power Generation,2014,43(10):46-50.
[8]罗嘉,陈世和,张曦,等.电站锅炉典型热工参数软测量研究[J].中国电力,2016,49(6):48-52.LUO Jia,CHEN Shihe,ZHANG Xi,et al.Research on soft measurement technology of typical thermal parameters of utility boilers[J].Electric Power,2016,49(6):48-52.
[9]TIPPING M E,SMOLA A.Sparse Bayesian learning and the relevance vector machine.[J].Journal of Machine Learning Research,2001,1(3):211-244.
[10]张亚男,杨慧中.基于RVM组合核优化的软测量模型研究[J].系统仿真学报,2018(1):272-277.ZHANG Yanan,YANG Huizhong.RVM soft sensing model based on optimized combined kernel function[J].Journal of System Simulation,2018(1):272-277.
[11]PSORAKIS I,DAMOULAS T,GIROLAMI M A.Multiclass relevance vector machines:an evaluation of sparsity and accuracy[J].IEEE Transactions on Neural Networks,2010,21(10):1588-1598.
[12]MIRJALILI S,LEWIS A.The whale optimization algorithm[J].Advances in Engineering Software,2016,95:51-67.
[13]蔡丹,季晓勇,史贺,等.改进分段Logistic混沌映射的方法及其性能分析[J].南京大学学报(自然科学版),2016(5):809-815.CAI Dan,JI Xiaoyong,SHI He,et al.Method for improving piecewise Logistic chaotic map and its performance analysis[J].Journal of Nanjing University(Natural Sciences),2016(5):809-815.
[14]李健,张彬文.烟气含氧量软测量方法研究[J].自动化仪表,2016(6):83-85.LI Jian,ZHANG Binwen.Research on the soft sensing methods of oxygen content in flue gas[J].Process Automation Instrumentation,2016(6):83-85.
[15]牛玉广,彭范.飞灰含碳量微波频率响应特性研究[J].仪器仪表学报,2017,38(11):2634-2642.NIU Yuguang,PENG Fan.Microwave frequency response characteristic of fly ash with different carbon content[J].Chinese Journal of Scientific Instrument,2017,38(11):2634-2642.