融合TVF-EMD和CNN-GRU锅炉受热面能效预测  

Fusion of TVF-EMD and CNN-GRU boiler heat surface energy efficiency prediction

作　　者：王楠 胡永涛 王康杰 崔方舒 史元浩 Wang Nan;Hu Yongtao;Wang Kangjie;Cui Fangshu;Shi Yuanhao(School of Electrical and Control Engineering,North University of China,Taiyuan 030051,China;School of Computer Science and Technology,North University of China,Taiyuan 030051,China)

机构地区：[1]中北大学电气与控制工程学院,太原030051 [2]中北大学计算机科学与技术学院,太原030051

出　　处：《电子测量与仪器学报》2024年第9期67-75,共9页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金(72071183);山西省基础研究计划(202303021222084);山西省研究生创新项目基金(2023SJ232)项目资助。

摘　　要：电站锅炉受热面积灰是导致锅炉热效率降低,甚至影响锅炉出力的重要因素之一,因此对锅炉受热面积灰结垢状况的精准预测是优化提升锅炉能效的前提。针对这一问题,本文以300 MW电站锅炉省煤器的清洁因子为研究对象,提出了一种融合基于时变滤波器的经验模态分解(time-varying filtered empirical mode decomposition,TVF-EMD)和卷积门控神经单元(convolutional neural network gated recurrent unit,CNN-GRU)的组合模型来预测锅炉受热面能效变化情况。首先通过改进的小波阈值法对非线性非平稳的清洁因子原始数据进行预处理,去除噪声和异常值;再通过TVF-EMD对处理后的数据进行分解得到预设好的本征模态分量,根据自相关系数对阈值大于0.2的分量进行叠加重构,在保留原始数据重要特征的同时降低相关性低的分量对预测精度的影响;最终将重构后的信号利用卷积神经网络(convolutional neural network,CNN)强大的特征提取能力和门控循环单元(gated recurrent unit,GRU)的时序记忆能力,建立输入与输出之间的非线性关系,实现较为精准的时间序列预测。研究结果表明,利用TVF-EMD进行分解相比直接进行预测可以提升9.62867%的预测精度,为后续优化及制订更加合理的吹灰策略提供了理论和技术支持。Power station boiler heated area ash is one of the important factors leading to the reduction of boiler thermal efficiency and even affecting boiler output,so accurate prediction of boiler heated area ash fouling condition is a prerequisite for optimizing and improving boiler energy efficiency.To address this problem,the paper takes the cleanliness factor of the economizer of a 300MW power station boiler as the research object,and proposes a combined model integrating time-varying filter-based empirical mode decomposition(TVF-EMD)and convolutionally gated neural unit(CNN-GRU)to predict the change of boiler heated surface energy efficiency.Firstly,the non-linear and non-smooth cleanliness factor raw data are preprocessed by the improved wavelet threshold method to remove noise and outliers;then the processed data are decomposed by TVF-EMD to obtain the preset intrinsic modal components,and the components with the threshold value greater than 0.2 are superimposed and reconstructed according to the autocorrelation coefficient,so as to reduce the impact of the low correlation components on the prediction accuracy while retaining the important features of the raw data;finally,the reconstructed model is combined with convolutional gated neural unit(CNN-GRU)to predict the energy efficiency change of the boiler heating surface.Finally,the reconstructed signal is used to establish the nonlinear relationship between input and output by using the powerful feature extraction capability of convolutional neural network(CNN)and the temporal memory capability of gated recurrent unit(GRU)to achieve more accurate time series prediction.The results show that the decomposition using TVF-EMD can improve the prediction accuracy by 9.62867%compared with the direct prediction,which provides theoretical and technical support for the subsequent optimization and the formulation of more reasonable soot-blowing strategies.

关 键 词：清洁因子 时变滤波器 经验模态分解 小波阈值法 卷积门控神经单元 

分 类 号：TM621.2[电气工程—电力系统及自动化] TP183[自动化与计算机技术—控制理论与控制工程]