摘要
以某600 MW亚临界火电机组凝结水系统为研究对象,以实现基于机器学习算法和专家知识的故障诊断和自动事故处理为目的,通过对其历史运行数据的选取、清洗建立适合数据分析和模型训练的样本集,采用核主成分分析算法搭建凝结水泵运行特性预警模型。通过模型对凝结水泵运行参数偏离正常值进行预警,并将预警结果和相关参数进行逻辑整合作为设备故障诊断的判据,最后再将判据作为自动事故处理的触发条件,实现了凝结水泵的预警、故障诊断和事故自动处理的全过程自动控制,结果表明模型对凝结水泵关键参数重构精度大于95%,能够对凝结水泵进气和出力异常做出准确诊断,提高了凝结水系统自动控制和智能化水平,具备实际工程应用价值。
Taking the condensate water system of a 600 MW subcritical thermal power unit as the research object,in order to realize fault diagnosis and automatic accident treatment based on machine learning algorithm and expert knowledge,the sample set suitable for data analysis and model training is established by selecting and cleaning the historical operation data,and the kernel principal component analysis algorithm is used to build the early warning model of condensate pump operation characteristics.The model is used to warn the deviation of the operating parameters of the condensate pump from the normal value,and the early warning results and related parameters are logically integrated as the criterion for equipment fault diagnosis.Finally,the criterion is used as the trigger condition for automatic accident treatment,and the whole process of automatic control of the early warning,fault diagnosis and automatic accident treatment of the condensate pump is realized.The results show that the reconstruction accuracy of the model for the key parameters of the condensate pump is greater than 95%,which can accurately diagnose the abnormal intake and output of the condensate pump,improve the automatic control and intelligent level of the condensate system,and have practical engineering application value.
作者
贾志军
白德龙
宋燕杰
王剑飞
李春新
JIA Zhijun;BAI Delong;SONG Yanjie;WANG Jianfei;LI Chunxin(Inner Mongolia Jinglong Electric Power Generation Company Limited,Ulanqab 012100,China;Inner Mongolia Vocational College of Chemical Engineering,Hohhot 010070,China)
出处
《华电技术》
CAS
2021年第8期48-53,共6页
HUADIAN TECHNOLOGY
关键词
大数据
核主成分分析
故障诊断
凝结水系统
自动事故处理
机器学习
智慧电厂
big data
kernel principal component analysis
fault diagnosis
condensate system
automatic accident treatment
machine learning
smart power plant
