Clean Coal Technology

2022, v.28;No.142(06) 206-214

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基于PCA-GRNN磨煤机安全性评估预测模型
Predictive model of coal mill safety evaluation based on PCA-GRNN

陈波;徐文韬;黄亚继;曹歌瀚;管诗骈;岳峻峰;
CHEN Bo;XU Wentao;HUANG Yaji;CAO Gehan;GUAN Shipian;YUE Junfeng;Jiangsu Frontier Electric Technology Co.,Ltd.;Key Laboratory of Energy Thermal Conversion and Process Measurement and Control of Ministry of Education,Southeast University;

摘要(Abstract):

磨煤机作为锅炉运行的重要辅机设备,其性能安全直接影响整个火电机组运行安全。针对电厂磨煤机安全性评估无法实时反馈的问题,结合主成分分析(PCA)与广义回归神经网络(GRNN)建立磨煤机安全性评估预测模型。首先,以磨煤机设备的实测运行数据为试验样本,基于PCA对影响磨煤机设备安全运行的众多变量开展主成分分析;其次,基于GRNN建立磨煤机安全性评估预测模型,其中以主成分为输入参数,对应的历史专家评分为输出参数,采用“留一法”划分训练样本和测试样本提高网络模型的训练精度;最后,基于GRNN、PCA-BP神经网络和BP神经网络分别建立磨煤机安全性评估预测模型且比较4个预测模型平均相对误差和耗费时间成本。结果表明:PCA提取的3个主成分F1、F2及F3的方差贡献率达96.55%;基于PCA-GRNN神经网络建立磨煤机安全性评估预测模型的平均相对误差最小,且耗费较少的时间成本;验证了基于PCA-GRNN神经网络建立磨煤机安全性评估预测模型的有效性。
The coal mill is an important auxiliary equipment for the operation of the boiler, and its performance safety directly affects the safety of the entire thermal power plant. Since the safety evaluation of coal mills in power plants cannot be fed back in real time, a predictive model for safety evaluation of coal mill was established by combining principal component analysis(PCA) with generalized regression neural network(GRNN).Firstly, the actual operation data of coal mill equipment were regarded as experimental samples, and principal component analysis was used to analyze the principal component for many variables affecting the safety of coal mill. Secondly, the safety evaluation and prediction model of coal mill was constructed based on generalized regression neural network(GRNN), and the crucial components were considered as the input variable, and the corresponding historical expert rating was treated as output variable, and the leave-one-out method was adopted to divide training samples and test samples to improve the training accuracy of network model. Finally, the safety evaluation prediction models were established based on GRNN neural network, PCA-BP neural network and BP neural network. The relative errors and time cost of the four prediction models were compared, respectively.The results show that the variance contribution rate of the three principal components F1, F2 and F3 extracted by PCA reaches 96.55%.Based on PCA-GRNN neural network, the average relative error of the prediction model for coal mill safety assessment is minimal and less time costly.The effectiveness of the predictive model of coal mill′s safety evaluation established by PCA-GRNN neural network is verified.

关键词(KeyWords): 磨煤机;主成分分析;广义回归神经网络;安全性评估;预测模型;留一法
coal mill;principal component analysis;generalized regression neural network;performance evaluation;prediction model;leave-one-out

Abstract:

Keywords:

基金项目(Foundation): 江苏方天电力技术有限公司科技资助项目(KJ201927);; 江苏省科技成果转化专项基金资助项目(BA2020001)

作者(Authors): 陈波;徐文韬;黄亚继;曹歌瀚;管诗骈;岳峻峰;
CHEN Bo;XU Wentao;HUANG Yaji;CAO Gehan;GUAN Shipian;YUE Junfeng;Jiangsu Frontier Electric Technology Co.,Ltd.;Key Laboratory of Energy Thermal Conversion and Process Measurement and Control of Ministry of Education,Southeast University;

DOI: 10.13226/j.issn.1006-6772.21050601

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