数据挖掘在火力发电厂中的应用研究
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摘要
火力发电厂实时数据库系统存储了大量DCS(Distributed Control System)系统的数据,这些数据的背后隐藏着许多有益于提高火力发电厂运行效率和安全的信息。但由于没有被深刻理解和挖掘,不可避免地造成了数据资源的巨大浪费。数据挖掘技术能够从数据中自动地提取知识,本文致力于研究数据挖掘技术在火力发电厂中的应用,以充分发挥存储在数据库中大量DCS数据对电力生产的指导作用。
本文围绕数据挖掘技术,考察和探讨了数据挖掘技术在火力发电厂中的应用现状,并主要研究了数据挖掘技术中的数据预处理技术、模糊C均值聚类算法和OLAP等技术及其应用。
针对火力发电厂的数据特性,本文尝试将数据预处理技术应用到实时数据库的数据处理中去,使数据的有效性得到保证,为数据挖掘的进一步工作打下基础。本文在对凝汽器性能分析的基础上,尝试构建了凝汽器多维数据模型,并应用模糊C均值聚类算法对多维数据集进行处理。在此基础上,在Excel的数据透视表中进行了以凝汽器真空为主题的OLAP分析,通过切片、钻取和旋转等方法可从多角度、多侧面去观察数据,从而挖掘出数据中所隐含的规律和知识。
There is a large numbers of DCS (Distributed Control System) system data collected in real-time database of thermal power plant. There are abundant and valuable information, which benefits to enhancing the efficiency and safety of operation in thermal power plant, hidden behind these data. But the data resource are wasted generally because not be used for comprehend and mining effectively. The Data Mining techniques can intelligently and automatically discover knowledge from data. In this paper, the application of data mining techniques in thermal power plant will be researched, in the interest of full use the function of DCS system data collected in database to direct the power generation.
This paper discussed the applied actuality of data mining techniques in the thermal power plant, and mostly researchs the data preprocessing techniques、the fuzzy c-means algorithm、OLAP(online analytical processing) and the application of these techniques.
Considering the characteristic of the data in thermal power plant, this paper attempt to apply the data preprocessing techniques to the data process of real-time database for guarantees the validity of data, so based the foundation for the father work of data mining. This paper attempt to establish the multi-dimensional data model based on the analysis of the condenser performance, and the fuzzy c-means algorithm to be used for the process of the multi-dimensional data. On the base of the data that have been processed, made an multi-dimensional analysis on the vacuum data with the OLAP (online analytical processing) method in the pivot table of Excel, and the data can be viewed in different ways by use the OLAP operations such as slice、roll-up、drill-down and pivot, the rule and the knowledge behind these data can be mined finally.
本文围绕数据挖掘技术,考察和探讨了数据挖掘技术在火力发电厂中的应用现状,并主要研究了数据挖掘技术中的数据预处理技术、模糊C均值聚类算法和OLAP等技术及其应用。
针对火力发电厂的数据特性,本文尝试将数据预处理技术应用到实时数据库的数据处理中去,使数据的有效性得到保证,为数据挖掘的进一步工作打下基础。本文在对凝汽器性能分析的基础上,尝试构建了凝汽器多维数据模型,并应用模糊C均值聚类算法对多维数据集进行处理。在此基础上,在Excel的数据透视表中进行了以凝汽器真空为主题的OLAP分析,通过切片、钻取和旋转等方法可从多角度、多侧面去观察数据,从而挖掘出数据中所隐含的规律和知识。
There is a large numbers of DCS (Distributed Control System) system data collected in real-time database of thermal power plant. There are abundant and valuable information, which benefits to enhancing the efficiency and safety of operation in thermal power plant, hidden behind these data. But the data resource are wasted generally because not be used for comprehend and mining effectively. The Data Mining techniques can intelligently and automatically discover knowledge from data. In this paper, the application of data mining techniques in thermal power plant will be researched, in the interest of full use the function of DCS system data collected in database to direct the power generation.
This paper discussed the applied actuality of data mining techniques in the thermal power plant, and mostly researchs the data preprocessing techniques、the fuzzy c-means algorithm、OLAP(online analytical processing) and the application of these techniques.
Considering the characteristic of the data in thermal power plant, this paper attempt to apply the data preprocessing techniques to the data process of real-time database for guarantees the validity of data, so based the foundation for the father work of data mining. This paper attempt to establish the multi-dimensional data model based on the analysis of the condenser performance, and the fuzzy c-means algorithm to be used for the process of the multi-dimensional data. On the base of the data that have been processed, made an multi-dimensional analysis on the vacuum data with the OLAP (online analytical processing) method in the pivot table of Excel, and the data can be viewed in different ways by use the OLAP operations such as slice、roll-up、drill-down and pivot, the rule and the knowledge behind these data can be mined finally.
引文
[1]朱明,数据挖掘,合肥:中国科学技术大学出版社,2002.5
[2]U.Fayyad,G.Piatetsky-Shapiro,P.Smyth,R.Uthurusamy,eds,Advances in knowledgeDiscovery and Data Mining,MIT Press,Cambrige,MA,1996
[3]熊伟,不完整关系数据库中关联规则挖掘问题的研究,华中师范大学电路与系统,2001.5
[4]陈坚红,任浩仁等,基于汽轮机运行监测数据的知识发现和数据挖掘初探,浙江电力,2001.6
[5]付忠广,田志友等,关联规则数据挖掘及其在电厂DCS数据分析中的应用,发电设备(2004增刊)
[6]王培红,陈强等,数据挖掘及其在电厂SIS中的应用,电力系统自动化,2004.4,第8期
[7]Ogilvie,Tony,E Swidenbank,B.W Hogg,Use of data mining techniques in the performance monitoring and optimization of a thermal power plant,IEEE 1998 The Institution ofElectrical Engineers,7/1-7/4 0963-3308
[8]靳涛,数据挖掘及在电厂凝汽设备诊断中的应用,(硕士学位论文),华北电力大学动力工程系,2003.1
[9]林宇等,数据仓库原理与实践,人民邮电出版社,2003.1
[10]嘉兴发电有限责任公司实时决策辅助系统(TPRI_ODSS)用户使用手册,苏州国电热工研究院有限公司,2002.12
[11]Jiawei Han,Micheline Kamber,Data Mining:Concepts and Techniques,第3版,范明,孟小峰等译.北京,机械工业出版社,2001.8
[12]Jiawei Han,Micheline Kamber,Data Mining:Concepts and Techniques(影印版).北京,高等教育出版社,2001
[13]毛国君,段立娟等,数据挖掘原理与算法.北京,清华大学出版社,2005.7
[14]行小帅,焦李成,数据挖掘的聚类方法,电路与系统学报,2003.2
[15]Bezdek J C,Pattern Recognition with Fuzzy Objective Function Algorithms.New York:Plenum Press,1981
[16]N R Pal,J C Bezdek,On cluster validity for the fuzzy c-means model[J],IEEE Trans Fuzzy Systems,1995.3(3):370-379
[17]Cheung Y S,Chart K P,Modified Fuzzy ISODATA for the Classification of Handwritten Chinese Characters,In:Proc 1986 Int Conf Chinese Comput,Singapore,1986:361-364
[18]高新波,李浩,模糊C均值聚类算法中参数m的优选,模式识别与人工智能,2000.1
[19]于剑,论模糊C均值算法的模糊指标,计算机学报,2003.8
[20]于剑,程乾生,关于FCM算法中权重指数m的一点注记,电子学报,2003.3
[21]Eva Part-Enander,The MATLAB 5 Handbook,第1版,王艳清等译,北京,机械工业出版社,2000.5
[22]苏金明,张莲花等,MATLAB工具箱应用,北京,电子工业出版社,2004.1
[23]张卓澄,大型电站凝汽器,北京,机械工业出版社,1993
[24]武森,M.巴斯蒂安[德]等,数据仓库与数据挖掘,北京,冶金工业出版社,2003.9
[25]夏火松等,数据仓库与数据挖掘技术,北京,科学出版社,2004
[26]杨世莹,Excel数据统计与分析范例应用[M],中国青年出版社,2005.1
[27]蒋丽,沈勇,Excel 2002的数据透视功能,微机发展,2003.12
[28]杜国宁,朱仲英,基于OLAM的决策支持系统,微型电脑应用,2004.6
[2]U.Fayyad,G.Piatetsky-Shapiro,P.Smyth,R.Uthurusamy,eds,Advances in knowledgeDiscovery and Data Mining,MIT Press,Cambrige,MA,1996
[3]熊伟,不完整关系数据库中关联规则挖掘问题的研究,华中师范大学电路与系统,2001.5
[4]陈坚红,任浩仁等,基于汽轮机运行监测数据的知识发现和数据挖掘初探,浙江电力,2001.6
[5]付忠广,田志友等,关联规则数据挖掘及其在电厂DCS数据分析中的应用,发电设备(2004增刊)
[6]王培红,陈强等,数据挖掘及其在电厂SIS中的应用,电力系统自动化,2004.4,第8期
[7]Ogilvie,Tony,E Swidenbank,B.W Hogg,Use of data mining techniques in the performance monitoring and optimization of a thermal power plant,IEEE 1998 The Institution ofElectrical Engineers,7/1-7/4 0963-3308
[8]靳涛,数据挖掘及在电厂凝汽设备诊断中的应用,(硕士学位论文),华北电力大学动力工程系,2003.1
[9]林宇等,数据仓库原理与实践,人民邮电出版社,2003.1
[10]嘉兴发电有限责任公司实时决策辅助系统(TPRI_ODSS)用户使用手册,苏州国电热工研究院有限公司,2002.12
[11]Jiawei Han,Micheline Kamber,Data Mining:Concepts and Techniques,第3版,范明,孟小峰等译.北京,机械工业出版社,2001.8
[12]Jiawei Han,Micheline Kamber,Data Mining:Concepts and Techniques(影印版).北京,高等教育出版社,2001
[13]毛国君,段立娟等,数据挖掘原理与算法.北京,清华大学出版社,2005.7
[14]行小帅,焦李成,数据挖掘的聚类方法,电路与系统学报,2003.2
[15]Bezdek J C,Pattern Recognition with Fuzzy Objective Function Algorithms.New York:Plenum Press,1981
[16]N R Pal,J C Bezdek,On cluster validity for the fuzzy c-means model[J],IEEE Trans Fuzzy Systems,1995.3(3):370-379
[17]Cheung Y S,Chart K P,Modified Fuzzy ISODATA for the Classification of Handwritten Chinese Characters,In:Proc 1986 Int Conf Chinese Comput,Singapore,1986:361-364
[18]高新波,李浩,模糊C均值聚类算法中参数m的优选,模式识别与人工智能,2000.1
[19]于剑,论模糊C均值算法的模糊指标,计算机学报,2003.8
[20]于剑,程乾生,关于FCM算法中权重指数m的一点注记,电子学报,2003.3
[21]Eva Part-Enander,The MATLAB 5 Handbook,第1版,王艳清等译,北京,机械工业出版社,2000.5
[22]苏金明,张莲花等,MATLAB工具箱应用,北京,电子工业出版社,2004.1
[23]张卓澄,大型电站凝汽器,北京,机械工业出版社,1993
[24]武森,M.巴斯蒂安[德]等,数据仓库与数据挖掘,北京,冶金工业出版社,2003.9
[25]夏火松等,数据仓库与数据挖掘技术,北京,科学出版社,2004
[26]杨世莹,Excel数据统计与分析范例应用[M],中国青年出版社,2005.1
[27]蒋丽,沈勇,Excel 2002的数据透视功能,微机发展,2003.12
[28]杜国宁,朱仲英,基于OLAM的决策支持系统,微型电脑应用,2004.6