基于可解释关系模型的SCR入口气温预测  

Prediction of SCR Inlet Gas Temperature Based on Interpretable Relationship Model

作　　者：路宽 杨兴森 张绪辉 孙雯雪 王海仰 杨子江 LU Kuan;YANG Xingsen;ZHANG Xuhui;SUN Wenxue;WANG Haiyang;YANG Zijiang(State Grid Shandong Electric Power Research Institute,Jinan 250003,China;State Grid Shandong Electric Power Company Jinan Zhangqiu District Power Supply Company,Jinan 250020,China;Huadian Qingdao Power Corporation,Qingdao 370200,China;College of Electrical Engineering and Automation,Shandong University of Science and Technology,Qingdao 266590,China)

机构地区：[1]国网山东省电力公司电力科学研究院,山东济南250003 [2]国网山东省电力公司济南市章丘区供电公司,山东济南250020 [3]华电青岛发电有限公司,山东青岛370200 [4]山东科技大学电气与自动化工程学院,山东青岛266590

出　　处：《山东电力技术》2024年第5期63-72,共10页Shandong Electric Power

基　　金：国家自然科学基金项目(62273214);山东省自然科学基金项目(ZR2023MF083)。

摘　　要：新能源在全社会发电中占比不断增高,为了能够辅助电网进行削峰填谷,火电机组需要增加在低负荷范围内的运行时间,这会对选择性催化还原(selective catalytic reduction,SCR)的脱硝效果产生负面影响。因此,对SCR入口烟气温度(SCR gas temperature,SCRIGT)进行准确预测非常重要。首先,采用极限梯度提升(extreme gradient boosting,XGBOOST)模型,以电厂的运行参数作为输入,功率与SCRIGT的比值作为输出进行预测,然后计算得到SCRIGT。结果显示,对于两个不同锅炉类型的火电机组,平均绝对百分比误差(mean absolute percentage error,MAPE)分别为3.07%和2.49%。其次,采用不可知模型的局部可解释(local interpretable model-agnostic explanations,LIME)算法分析XGBOOST模型的预测结果,显示功率和功率与SCRIGT的比值之间存在线性关系,且R2(R-squared)为0.994,基于此构建了一种可解释关系模型进行SCRIGT预测。最后,比较分析显示,对于试验中的两个机组,可解释关系模型预测结果的MAPE分别改进至0.68%和0.97%。The proportion of new energy in the power generation in the whole society is constantly increasing.To shave peaks and fill valleys,the thermal power plants must increase their operating time in the low-load range,which has a negative effect on the denitrification of selective catalytic reduction(SCR).Therefore,accurate prediction of SCR gas temperature(SCRIGT)at the inlet is crucial.Firstly,using the operating parameters of the power plant as input and the ratio of power to SCRIGT as output,the XGBOOST model was used for prediction to obtain SCRIGT.The results show that for two different types of thermal power units,the average absolute percentage error(MAPE)is 3.07%and 2.49%,respectively.Then,the local interpretable model agnostic explanations(LIME)was applied to explain the prediction result of XGBOOST model,which finds a linear relationship between load and load∙SCRIGT-1 with R-squared of 0.994,based on which an interpretable relationship model was constructed.Finally,the comparative analysis shows that the mean absolute percentage errors of the interpretable relationship model have been improved to 0.68%and 0.97%,respectively.

关 键 词：XGBOOST模型 LIME算法 SCR入口烟气温度 火电机组 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]