Research on on-line fault diagnosis and treatment of power plant equipment based on KPCA

doi:10.3969/j.issn.1674-1951.2021.08.007

Abstract

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

CLC Number:

TM621:TK321

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.

Figures/Tables 12

Fig.1

Fig.2

Tab.1

Tab.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Tab.3

Tab.4

Fig.8

References 18

[1]	张晋宾, 周四维. 智能电厂概念、体系架构及核心技术[J]. 热力发电, 2019, 48(9):9-13.
	ZHANG Jinbin, ZHOU Siwei. Concept,architecture and core technology of smart power plant[J]. Thermal Power Generation, 2019, 48(9):9-13.
[2]	郭为民, 张广涛, 李炳楠, 等. 火电厂智能化建设规划与技术路线[J]. 中国电力, 2018, 51(10):17-25.
	GUO Weimin, ZHANG Guangtao, LI Bingnan, et al. Construction planning and technical route for thermal power plant intelligentization[J]. Electric Power, 2018, 51(10):17-25.
[3]	刘吉臻, 胡勇, 曾德良, 等. 智能发电厂的架构及特征[J]. 中国电机工程学报, 2017, 37(22):6463-6470.
	LIU Jizhen, HU Yong, ZENG Deliang, et al. Architecture and feature of smart power generation[J]. Proceedings of the CSEE, 2017, 37(22):6463-6470.
[4]	杨晓巳, 陶新磊, 韩立. 虚拟电厂技术现状及展望[J]. 华电技术, 2020, 42(5):73-78.
	YANG Xiaosi, TAO Xinlei, HAN Li. Status and prospect of virtual power plant technology[J]. Huadian Technology, 2020, 42(5):73-78.
	YANG Xiaosi, TAO Xinlei, HAN Li. Status and prospect of virtual power plant technology[J]. Huadian Technology, 2020, 42(5):73-78.
[5]	李晓杰, 田亮. 基于多尺度主元分析的磨煤机堵塞检测[J]. 电力科学与工程, 2018, 34(1):41-45.
	李晓杰, 田亮. 基于多尺度主元分析的磨煤机堵塞检测[J]. 电力科学与工程, 2018, 34(1):41-45.
	LI Xiaojie, TIAN Liang. Blockage detection of coal mill based on multi-scale principal component analysis[J]. Electric Power Science and Engineering, 2018, 34(1):41-45.
	LI Xiaojie, TIAN Liang. Blockage detection of coal mill based on multi-scale principal component analysis[J]. Electric Power Science and Engineering, 2018, 34(1):41-45.
[6]	张承彪, 罗运柏, 文习山. 主成分分析在变压器故障诊断中的应用研究[J]. 高电压技术, 2005, 31(8):9-11.
	张承彪, 罗运柏, 文习山. 主成分分析在变压器故障诊断中的应用研究[J]. 高电压技术, 2005, 31(8):9-11.
	ZHANG Cheng biao, LUO Yunbai, WEN Xishan. Study on fault diagnosis of transformer based on principal component analysis of dissolved gas[J]. High Voltage Engineering, 2005, 31(8):9-11.
	ZHANG Cheng biao, LUO Yunbai, WEN Xishan. Study on fault diagnosis of transformer based on principal component analysis of dissolved gas[J]. High Voltage Engineering, 2005, 31(8):9-11.
[7]	张世荣, 程琴, 张芳芳. 基于感测线圈和KPCA的电机轴承故障检测[J]. 电机与控制应用, 2018, 45(4):98-104.
	张世荣, 程琴, 张芳芳. 基于感测线圈和KPCA的电机轴承故障检测[J]. 电机与控制应用, 2018, 45(4):98-104.
	ZHANG Shirong, CHENG Qin, ZHANG Fangfang. Fault detection of motor bearings based on detection coils and KPCA algorithm[J]. Electric Machines & Control Application, 2018, 45(4):98-104.
	ZHANG Shirong, CHENG Qin, ZHANG Fangfang. Fault detection of motor bearings based on detection coils and KPCA algorithm[J]. Electric Machines & Control Application, 2018, 45(4):98-104.
[8]	姜健. 基于改进PCA算法的航空发动机状态诊断模型[J]. 燃气涡轮试验与研究, 2017, 30(2):32-36.
	姜健. 基于改进PCA算法的航空发动机状态诊断模型[J]. 燃气涡轮试验与研究, 2017, 30(2):32-36.
	JIANG Jian. Modeling of aero-engine state diagnosis based on improved PCA algorithm[J]. Gas Turbine Experiment and Research, 2017, 30(2):32-36.
	JIANG Jian. Modeling of aero-engine state diagnosis based on improved PCA algorithm[J]. Gas Turbine Experiment and Research, 2017, 30(2):32-36.
[9]	李传坤, 赵东风, 王春利, 等. 一种化工过程故障诊断知识自动化方法研究[J]. 高校化学工程学报, 2018, 32(2):393-400.
	李传坤, 赵东风, 王春利, 等. 一种化工过程故障诊断知识自动化方法研究[J]. 高校化学工程学报, 2018, 32(2):393-400.
	LI Chuankun, ZHAO Dongfeng, WANG Chunli, et al. Study on a knowledge automation method for fault diagnosis of chemical processes[J]. Journal of Chemical Engineering of Chinese Universities, 2018, 32(2):393-400.
	LI Chuankun, ZHAO Dongfeng, WANG Chunli, et al. Study on a knowledge automation method for fault diagnosis of chemical processes[J]. Journal of Chemical Engineering of Chinese Universities, 2018, 32(2):393-400.
[10]	杜海莲, 苗诗瑜, 杜文霞, 等. 改进主元分析方法及数据重构在工业系统中的故障诊断研究[J]. 南京理工大学学报, 2019, 43(1):72-77,85.
	杜海莲, 苗诗瑜, 杜文霞, 等. 改进主元分析方法及数据重构在工业系统中的故障诊断研究[J]. 南京理工大学学报, 2019, 43(1):72-77,85.
	DU Hailian, MIAO Shiyu, DU Wenxia, et al. Research on fault diagnosis of industrial process based on improved PCA method and data reconstruction[J]. Journal of Nanjing University of Science and Technology, 2019, 43(1):72-77,85.
	DU Hailian, MIAO Shiyu, DU Wenxia, et al. Research on fault diagnosis of industrial process based on improved PCA method and data reconstruction[J]. Journal of Nanjing University of Science and Technology, 2019, 43(1):72-77,85.
[11]	高会, 李彦平. 基于残差主元分析的多变量系统故障诊断方法及应用[J]. 沈阳大学学报(自然科学版), 2016, 28(2):141-146.
	高会, 李彦平. 基于残差主元分析的多变量系统故障诊断方法及应用[J]. 沈阳大学学报(自然科学版), 2016, 28(2):141-146.
	GAO Hui, LI Yanping. Multivariate system fault diagnosis methods and its applications based on residual principal component analysis[J]. Journal of Shenyang University(Natural Science), 2016, 28(2):141-146.
	GAO Hui, LI Yanping. Multivariate system fault diagnosis methods and its applications based on residual principal component analysis[J]. Journal of Shenyang University(Natural Science), 2016, 28(2):141-146.
[12]	牛征. 基于多元统计分析的火电厂控制系统故障诊断研究[D]. 北京:华北电力大学, 2006.
	牛征. 基于多元统计分析的火电厂控制系统故障诊断研究[D]. 北京:华北电力大学, 2006.
[13]	薛盛炜, 李川, 李英娜. 改进模糊聚类与主成分分析下的变压器故障识别[J]. 河南科技大学学报(自然科学版), 2020, 41(6):39-44,50.
	薛盛炜, 李川, 李英娜. 改进模糊聚类与主成分分析下的变压器故障识别[J]. 河南科技大学学报(自然科学版), 2020, 41(6):39-44,50.
[14]	贾子文, 顾煜炯. 基于改进的多块核主元分析的风电机组故障诊断方法[J]. 动力工程学报, 2018, 38(10):820-828.
	贾子文, 顾煜炯. 基于改进的多块核主元分析的风电机组故障诊断方法[J]. 动力工程学报, 2018, 38(10):820-828.
	JIA Ziwen, GU Yujiong. Fault Diagnosis of wind turbines based on improved multiblock kernel principal component analysis[J]. Journal of Chinese Society of Power Engineering, 2018, 38(10):820-828.
	JIA Ziwen, GU Yujiong. Fault Diagnosis of wind turbines based on improved multiblock kernel principal component analysis[J]. Journal of Chinese Society of Power Engineering, 2018, 38(10):820-828.
[15]	者娜, 杨剑锋, 刘文彬, 等. KPCA和改进SVM在滚动轴承剩余寿命预测中的应用研究[J]. 机械设计与制造, 2019(11):1-4,8.
	者娜, 杨剑锋, 刘文彬, 等. KPCA和改进SVM在滚动轴承剩余寿命预测中的应用研究[J]. 机械设计与制造, 2019(11):1-4,8.
	ZHE Na, YANG Jianfeng, LIU Wenbin, et al. Research on application of KPCA and improved SVM in residual life prediction of rolling bearings[J]. Machinery Design & Manufacture, 2019(11):1-4,8.
	ZHE Na, YANG Jianfeng, LIU Wenbin, et al. Research on application of KPCA and improved SVM in residual life prediction of rolling bearings[J]. Machinery Design & Manufacture, 2019(11):1-4,8.
[16]	高明明, 杨磊, 于浩洋, 等. 量子计算在火电机组优化控制中的应用综述[J]. 华电技术, 2020, 42(8):90-96.
	高明明, 杨磊, 于浩洋, 等. 量子计算在火电机组优化控制中的应用综述[J]. 华电技术, 2020, 42(8):90-96.
	GAO Mingming, YANG Lei, YU Haoyang, et al. Review on the application of quantum computing in optimization control on thermal power units[J]. Huadian Technology, 2020, 42(8):90-96.
	GAO Mingming, YANG Lei, YU Haoyang, et al. Review on the application of quantum computing in optimization control on thermal power units[J]. Huadian Technology, 2020, 42(8):90-96.
[17]	冯泽磊, 吴美凤. 基于大数据平台的火电机组实时运行状态监测[J]. 华电技术, 2019, 41(4):29-32,39.
	冯泽磊, 吴美凤. 基于大数据平台的火电机组实时运行状态监测[J]. 华电技术, 2019, 41(4):29-32,39.
	FENG Zelei, WU Meifeng. Real-time operation monitoring of thermal power units based on big data platform[J]. Huadian Technology, 2019, 41(4):29-32,39.
	FENG Zelei, WU Meifeng. Real-time operation monitoring of thermal power units based on big data platform[J]. Huadian Technology, 2019, 41(4):29-32,39.
[18]	柴天佑. 工业过程控制系统研究现状与发展方向[J]. 中国科学, 2016, 46(8):1003-1015.
	柴天佑. 工业过程控制系统研究现状与发展方向[J]. 中国科学, 2016, 46(8):1003-1015.
	CHAI Tianyou. Industrial process control systems:research status and development direction[J]. Scientia Sinica(Informationis), 2016, 46(8):1003-1015.
	CHAI Tianyou. Industrial process control systems:research status and development direction[J]. Scientia Sinica(Informationis), 2016, 46(8):1003-1015.

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