基于电厂数据的机组性能关联规则分析
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摘要
随着DCS系统在电力行业的普遍推广，电厂的DCS系统存储了大量的数据。这些数据的背后隐藏了许多对提高电厂的生产效率、经济安全性有积极的指导意义的信息。但由于没有被深刻理解和挖掘，不可避免的造成了数据资源的巨大浪费。数据挖掘是从大量数据中自动提取知识的过程。本文用数据挖掘技术中的关联规则挖掘技术进行挖掘，以充分发挥存储在电厂数据库中大量DCS数据对电力生产的指导作用。
论文首先研讨了关联规则的各种挖掘算法，如：经典Apriori算法、FP-growth算法、多层关联规则挖掘算法及多维关联规则挖掘算法，并开发了电厂机组运行数据关联规则挖掘系统软件（ARMS1.0）。论文首次尝试将关联规则挖掘技术应用于热力发电厂汽轮发电机组DCS运行数据的分析，获得了有益的分析结果。对挖掘结果的分析表明该技术可以用于电厂机组的性能分析、状态监测、故障诊断和状态检修等方面，可机组开展状态检修、开发电厂机组SIS系统提供技术支持，很有意义。
With the popularization of DCS system in electrical industry, there is a large number of DCS system data collected in power plant. There is lots of valuable information, which benefits to enhancing the economic performance and safety of power plant, hidden behind of the data. But because of not being mind and used effectively, there must be lots of data resource waste. Data mining techniques can intelligently and automatically discover knowledge from database. In this paper, association rules mining, which belongs to data mining techniques, is applied to make full use of the function of the data collected in DCS system in power plant, which can direct electric power production.
Firstly, in this paper, many kinds of association rules mining methods are discussed such as apriori arithmetic and FP-growth arithmetic and multilevel association rules mining arithmetic and multidimensional association rules mining arithmetic. Then the process of developing association rules mining system 1.0, which is towards the data in power plant, is introduced. In the paper, it is the first time that association rules mining is attempted to applied towards the analysis of the data of steam turbo-generator unit in thermal power plant, and valuable result has been gained. Through the analysis of the result, we found that this technique not only can be used in the analysis of the performance of the unit in power plant and condition monitoring and fault diagnosis and condition based
maintenance, but also can provide technical support towards realizing both condition based maintenance and SIS system in power plant. It is very significant.
Tian Zhiyou(Thermal engineering)
Directed by prof. Fu Zhongguang
论文首先研讨了关联规则的各种挖掘算法，如：经典Apriori算法、FP-growth算法、多层关联规则挖掘算法及多维关联规则挖掘算法，并开发了电厂机组运行数据关联规则挖掘系统软件（ARMS1.0）。论文首次尝试将关联规则挖掘技术应用于热力发电厂汽轮发电机组DCS运行数据的分析，获得了有益的分析结果。对挖掘结果的分析表明该技术可以用于电厂机组的性能分析、状态监测、故障诊断和状态检修等方面，可机组开展状态检修、开发电厂机组SIS系统提供技术支持，很有意义。
With the popularization of DCS system in electrical industry, there is a large number of DCS system data collected in power plant. There is lots of valuable information, which benefits to enhancing the economic performance and safety of power plant, hidden behind of the data. But because of not being mind and used effectively, there must be lots of data resource waste. Data mining techniques can intelligently and automatically discover knowledge from database. In this paper, association rules mining, which belongs to data mining techniques, is applied to make full use of the function of the data collected in DCS system in power plant, which can direct electric power production.
Firstly, in this paper, many kinds of association rules mining methods are discussed such as apriori arithmetic and FP-growth arithmetic and multilevel association rules mining arithmetic and multidimensional association rules mining arithmetic. Then the process of developing association rules mining system 1.0, which is towards the data in power plant, is introduced. In the paper, it is the first time that association rules mining is attempted to applied towards the analysis of the data of steam turbo-generator unit in thermal power plant, and valuable result has been gained. Through the analysis of the result, we found that this technique not only can be used in the analysis of the performance of the unit in power plant and condition monitoring and fault diagnosis and condition based
maintenance, but also can provide technical support towards realizing both condition based maintenance and SIS system in power plant. It is very significant.
Tian Zhiyou(Thermal engineering)
Directed by prof. Fu Zhongguang
引文
[1] 余勇，厂级监控信息系统方案研究，中国动力工程学会自动控制专委会，全国电力设计热控专业技术信息网，江苏省电机工程学会自动化与计算机应用专委会，东南大学动力工程系，电站自动化信息化学术技术交流会议论文集，南京，2002，1，4～27
[2] 许继刚，郑慧丽，电厂管理控制一体化信息系统的发展，电力系统自动化，（7），2001.4，59～63
[3] 吴今培，肖健华， 智能故障诊断与专家系统， 北京：科学出版社， 1997
[4] 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
[5] 张红先，陆佳政，曹一家，状态监测中专家系统的开发，2003，23（2）， 21～25
[6] 程芸，在电力市场环境下发电企业实施状态检修的理论及实证研究，（硕士学位论文），华北电力大学技术经济及管理，1999.12
[7] 张海琳，设备状态检修与传感器故障诊断技术研究，（硕士学位论文），华北电力大学控制理论与控制工程，2001.12
[8] 陈维荣，宋永华，孙锦鑫，电力系统设备状态监测的概念及现状，电网技术，2000，24（11），12～17
[9] Jiawei Han, Micheline Kamber, 数据挖掘概念与技术（范明,孟小峰等译）, 北京:机械工业出版社, 2001.08,3，149～184
[10] U.Fayyad, G.Piatetsky-Shapiro, P.Smyth, R.Uthurusamy, eds., Advances in knowledge Discovery and Data Mining, MIT Press, Cambrige, MA,1996
[11] 熊伟，不完整关系数据库中关联规则挖掘问题的研究，（硕士学位论文），华中师范大学电路与系统，2001.5
[12] 朱琰，多维关联规则的研究，（硕士学位论文），郑州大学计算机科学与技术，2001.5
[13] 宋国杰，多维关联规则挖掘研究，（硕士学位论文），郑州大学计算机科学与技术，2001.5
[14] 郝华风，基于联机分析的关联规则挖掘， (硕士学位论文)，郑州大学计算机科学与技术，2001.4
[15] Chen Jianhong, Knowledge discovery and data mining based on power plant real-time database: A survey [C], Proc. of International
Conference on Power Engineering, 2001
[16] 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 of Electrical Engineers, 7/1-7/4 0963-3308
[17] 单光先，任平，曹鸣，数据仓库与数据挖掘技术用于设备预测检修， 江苏电机工程，2003，22（1），1～3
[18] 李飞,陈梅, 数据挖掘中关联规则挖掘算法的改进及其应用,贵州大学学报(自然科学版),2003,20（2）,148～153
[19] 谈英姿,沈炯,吕震中, 面向工业领域的KDD技术及其应用前景, 工业控制计算机,2001,14（9）,44～48
[20] R. Agrawal, T. Imielinski, A. Swami, Mining association rules between sets of items in large databases, Proceedings of the ACM SIGMOD Conference on Management of data, 1993, 207～216,.
[21] Anthony K.H. Tung, Hongjun Lu, Jiawei Han, Ling Feng, Efficient Mining of Intertransaction Association Rules, IEEE Transactions on Knowledge and Data Engineering, 2003, vol15, page 43～56
[22] Krishnamoorthy Srikumar, Bharat Bhasker, Efficiently Mining Maximal Frequent Sets For Discovering Association Rules, IEEE Proceedings of the Seventh International Database Engineering and Application Symposium, 2003
[23] Shyue-Liang, Wang,Mei-Hwa Wang, Wen-Yang Lin, Tzung-Pei Hong, Efficient Generation of Adaptive-Support Association Rules, 2003, IEEE 0-7803-7952-7/03, 894～899
[24] Miguel Delgado, Nicolas Marin, Daniel Sanchez, Maria-Amparo Vila, Fuzzy Association Rules:General Model and Applications, IEEE Transactions on Fuzzy Systems, 2003, Vol.11, No.2, 214～225
[25] William Cheung, Osmar R.Zaiane, Incremental Mining of Frequent Patterns without Candidate Generation or Support Constraint, IEEE Proceedings of the Seventh International Database Engineering and Applications Symposium, 1098-8068/03, 2003
[26] Xiang-Rong Jiang, Le Gruenwald, Microarray Gene Expression Data Association Rules Mining Based on JG-Tree, IEEE Proceedings of the 14th International Workshop on Database and Expert Systems Applications, 2003,1529-4188/03
[27] 于达仁,王伟,徐志强,鲍文,基于关联规则的时延不良数据检验PCA方法，第四届全国火力发电技术学术年会论文，西安，2003，626～630
[28] 马元元,孙志挥, 增量关联规则在大型火力发电厂实时控制中的应用,工业控制机算机，2000，13（1），14～15
[29] A. Savasere, E. Omiecinski, S. Navathe, An efficient algorithm for mining association rules in large databases, Proceedings of the 21st International Conference on Very large Database, 1995
[30] J. S. Park, M. S. Chen, P. S. Yu, An effective hash-based algorithm for mining association rules, Proceedings of ACM SIGMOD International Conference on Management of Data, 1995.5, pages 175～186
[31] H. Mannila, H. Toivonen, A. Verkamo, Efficient algorithm for discovering association rules, AAAI Workshop on Knowledge Discovery in Databases, 1994, 181～192
[32] H. Toivonen, Sampling large databases for association rules, Proceedings of the 22nd International Conference on Very Large Database, Bombay, India,1996
[33] J. L. Lin, M.H.Dunham, Mining association rules: Anti-skew algorithms, Proceedings of the International Conference on Data Engingeering, Orlando, Florida，1998.02,
[34] 数据挖掘讨论组 ，http://www.dmgroup.org.cn/lw.htm，一个不需要产生候选集的频繁集产生算法的分析与实现
[35] 数据挖掘讨论组，http://www.dmgroup.org.cn/ppt.htm，关联规则
[36] J. Han, G. Karypis, V. Kumar, Calable Parallel Data Mining for Association Rules, In Proc.1997 ACM-SIGMOD Int, Conf Very Large Data Bases
[37] S.Agrawal, R.Srikant, Fast Algorithms for Mining Association Rules, In Proc.1994 Int, Conf.Very Large Data Base
[38] M. Kamber, J.Han, J. Y. Chiang, Metarules-guided Mining of Multidimensional Association Rules Using Data Cubes, In Proc.3rd Int.Conf.KDD’97
[39] 林金霖，Delphi6实务经典，北京，中国铁道出版社，2002，1～954
[40] 蒋方帅，Borland Delphi程序设计，北京，清华大学出版社，2002，1～476
[41] Jeffrey R.Shapiro,SQL Server 2000 参考大全（周之，黄玫译），北京，清华大学出版社，2002.6，1～675
[42] Mike Gunderloy,Joseph L.Jorden, SQL Server 2000从入门到精通（邱仲潘等译），北京，电子工业出版社，2001.3，1～745
[43] 柯慧新，黄京华，沈浩，统计分析法，北京，北京广播学院出版社，1992，465～484
[2] 许继刚，郑慧丽，电厂管理控制一体化信息系统的发展，电力系统自动化，（7），2001.4，59～63
[3] 吴今培，肖健华， 智能故障诊断与专家系统， 北京：科学出版社， 1997
[4] 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
[5] 张红先，陆佳政，曹一家，状态监测中专家系统的开发，2003，23（2）， 21～25
[6] 程芸，在电力市场环境下发电企业实施状态检修的理论及实证研究，（硕士学位论文），华北电力大学技术经济及管理，1999.12
[7] 张海琳，设备状态检修与传感器故障诊断技术研究，（硕士学位论文），华北电力大学控制理论与控制工程，2001.12
[8] 陈维荣，宋永华，孙锦鑫，电力系统设备状态监测的概念及现状，电网技术，2000，24（11），12～17
[9] Jiawei Han, Micheline Kamber, 数据挖掘概念与技术（范明,孟小峰等译）, 北京:机械工业出版社, 2001.08,3，149～184
[10] U.Fayyad, G.Piatetsky-Shapiro, P.Smyth, R.Uthurusamy, eds., Advances in knowledge Discovery and Data Mining, MIT Press, Cambrige, MA,1996
[11] 熊伟，不完整关系数据库中关联规则挖掘问题的研究，（硕士学位论文），华中师范大学电路与系统，2001.5
[12] 朱琰，多维关联规则的研究，（硕士学位论文），郑州大学计算机科学与技术，2001.5
[13] 宋国杰，多维关联规则挖掘研究，（硕士学位论文），郑州大学计算机科学与技术，2001.5
[14] 郝华风，基于联机分析的关联规则挖掘， (硕士学位论文)，郑州大学计算机科学与技术，2001.4
[15] Chen Jianhong, Knowledge discovery and data mining based on power plant real-time database: A survey [C], Proc. of International
Conference on Power Engineering, 2001
[16] 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 of Electrical Engineers, 7/1-7/4 0963-3308
[17] 单光先，任平，曹鸣，数据仓库与数据挖掘技术用于设备预测检修， 江苏电机工程，2003，22（1），1～3
[18] 李飞,陈梅, 数据挖掘中关联规则挖掘算法的改进及其应用,贵州大学学报(自然科学版),2003,20（2）,148～153
[19] 谈英姿,沈炯,吕震中, 面向工业领域的KDD技术及其应用前景, 工业控制计算机,2001,14（9）,44～48
[20] R. Agrawal, T. Imielinski, A. Swami, Mining association rules between sets of items in large databases, Proceedings of the ACM SIGMOD Conference on Management of data, 1993, 207～216,.
[21] Anthony K.H. Tung, Hongjun Lu, Jiawei Han, Ling Feng, Efficient Mining of Intertransaction Association Rules, IEEE Transactions on Knowledge and Data Engineering, 2003, vol15, page 43～56
[22] Krishnamoorthy Srikumar, Bharat Bhasker, Efficiently Mining Maximal Frequent Sets For Discovering Association Rules, IEEE Proceedings of the Seventh International Database Engineering and Application Symposium, 2003
[23] Shyue-Liang, Wang,Mei-Hwa Wang, Wen-Yang Lin, Tzung-Pei Hong, Efficient Generation of Adaptive-Support Association Rules, 2003, IEEE 0-7803-7952-7/03, 894～899
[24] Miguel Delgado, Nicolas Marin, Daniel Sanchez, Maria-Amparo Vila, Fuzzy Association Rules:General Model and Applications, IEEE Transactions on Fuzzy Systems, 2003, Vol.11, No.2, 214～225
[25] William Cheung, Osmar R.Zaiane, Incremental Mining of Frequent Patterns without Candidate Generation or Support Constraint, IEEE Proceedings of the Seventh International Database Engineering and Applications Symposium, 1098-8068/03, 2003
[26] Xiang-Rong Jiang, Le Gruenwald, Microarray Gene Expression Data Association Rules Mining Based on JG-Tree, IEEE Proceedings of the 14th International Workshop on Database and Expert Systems Applications, 2003,1529-4188/03
[27] 于达仁,王伟,徐志强,鲍文,基于关联规则的时延不良数据检验PCA方法，第四届全国火力发电技术学术年会论文，西安，2003，626～630
[28] 马元元,孙志挥, 增量关联规则在大型火力发电厂实时控制中的应用,工业控制机算机，2000，13（1），14～15
[29] A. Savasere, E. Omiecinski, S. Navathe, An efficient algorithm for mining association rules in large databases, Proceedings of the 21st International Conference on Very large Database, 1995
[30] J. S. Park, M. S. Chen, P. S. Yu, An effective hash-based algorithm for mining association rules, Proceedings of ACM SIGMOD International Conference on Management of Data, 1995.5, pages 175～186
[31] H. Mannila, H. Toivonen, A. Verkamo, Efficient algorithm for discovering association rules, AAAI Workshop on Knowledge Discovery in Databases, 1994, 181～192
[32] H. Toivonen, Sampling large databases for association rules, Proceedings of the 22nd International Conference on Very Large Database, Bombay, India,1996
[33] J. L. Lin, M.H.Dunham, Mining association rules: Anti-skew algorithms, Proceedings of the International Conference on Data Engingeering, Orlando, Florida，1998.02,
[34] 数据挖掘讨论组 ，http://www.dmgroup.org.cn/lw.htm，一个不需要产生候选集的频繁集产生算法的分析与实现
[35] 数据挖掘讨论组，http://www.dmgroup.org.cn/ppt.htm，关联规则
[36] J. Han, G. Karypis, V. Kumar, Calable Parallel Data Mining for Association Rules, In Proc.1997 ACM-SIGMOD Int, Conf Very Large Data Bases
[37] S.Agrawal, R.Srikant, Fast Algorithms for Mining Association Rules, In Proc.1994 Int, Conf.Very Large Data Base
[38] M. Kamber, J.Han, J. Y. Chiang, Metarules-guided Mining of Multidimensional Association Rules Using Data Cubes, In Proc.3rd Int.Conf.KDD’97
[39] 林金霖，Delphi6实务经典，北京，中国铁道出版社，2002，1～954
[40] 蒋方帅，Borland Delphi程序设计，北京，清华大学出版社，2002，1～476
[41] Jeffrey R.Shapiro,SQL Server 2000 参考大全（周之，黄玫译），北京，清华大学出版社，2002.6，1～675
[42] Mike Gunderloy,Joseph L.Jorden, SQL Server 2000从入门到精通（邱仲潘等译），北京，电子工业出版社，2001.3，1～745
[43] 柯慧新，黄京华，沈浩，统计分析法，北京，北京广播学院出版社，1992，465～484