基于KPCA算法的电厂设备在线故障诊断研究

doi:10.3969/j.issn.1674-1951.2021.08.007

摘要/Abstract

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。

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

Abstract:

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.

Key words: big data, kernel principal component analysis, fault diagnosis, condensate system, automatic accident treatment, machine learning, smart power plant

中图分类号:

TM621:TK321

贾志军, 白德龙, 宋燕杰, 王剑飞, 李春新. 基于KPCA算法的电厂设备在线故障诊断研究[J]. 华电技术, 2021, 43(8): 48-53.

JIA Zhijun, BAI Delong, SONG Yanjie, WANG Jianfei, LI Chunxin. Research on on-line fault diagnosis and treatment of power plant equipment based on KPCA[J]. Huadian Technology, 2021, 43(8): 48-53.

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项目	额定参数	运行范围
电流/A	168.0	19.0~65.0
运行频率/Hz	27.00~50.00	27.00~50.00
凝结水流量/(t·h^-1)	1 870	700~1 700
凝结水泵入口压力/kPa	—	-60.0~-40.0
^#5低压加热器出口压力/MPa	—	0.80~3.50

工况	工况区分
1	单泵变频,再循环全关,旁路全开
2	单泵变频,再循环全关,旁路全关
3	单泵变频,再循环全开,旁路全关
4	单泵工频,再循环全关,旁路全关

参数	故障幅度	重构偏差	重构精度/%
电流/A	5.0	0.8	97
频率/Hz	1.00	0.13	98
^#5低压加热器出口压力/MPa	0.15	0.01	97
凝结水泵入口压力/kPa	12.0	1.9	95

预警参数	预警值
A凝结水泵入口压力/kPa	+12.0
A凝结水泵电流/A	-5.0
A凝结水泵频率增大/Hz	+1.00