基于数据挖掘的变压器故障诊断和预测研究
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摘要
变压器是电力系统中最重要的电气设备之一,其运行状态直接影响系统的安全运行水平,变压器一旦发生事故,造成的直接和间接的经济损失是巨大的。变压器故障诊断和故障预测是保证其正常运行并进行状态维修的基础。本文就变压器数据仓库的构建、变压器综合故障诊断、变压器故障预测方法进行了深入的研究。
为了进行变压器状态和故障自动预测,以及便于维护人员对于变压器状态进行人工预测,提出了构建电力变压器数据仓库的数据处理思想,在对有关变压器信息的多种数据源进行分析和重新组织的基础上,利用联机分析处理和数据挖掘技术,可对变压器故障诊断和早期故障预测起到积极作用。
针对单一诊断模型受变压器试验数据量和色谱数据随机因素影响较大的缺点,提出了以多个贝叶斯网络分类器作为故障诊断模型群和利用支持向量机进行集成的变压器故障组合诊断方法,该方法可解决支持向量机进行多分类问题时训练效率低、求解精度差的问题。
针对已有变压器故障诊断方法在信息缺失多、有偏差的情况下误判率高的问题,提出了利用选择性贝叶斯分类器SRBC直接从不完整的变压器试验数据估算变量的概率分,进而估算其互信息的方法,该方法可解决已有变压器故障诊断方法在信息缺失多、有偏差的情况下误判率高的问题。
针对单一预测模型的适应性较差的缺点,提出了一个基于支持向量机的变压器故障组合预测模型及其求解步骤。在预测过程中,首先利用多个单一预测方法构成预测模型群,对原始油中溶解气体数据进行拟合。然后,把预测模型群的拟合结果作为支持向量机回归模型的输入进行二次预测,形成变权重的组合预测。
采用.NET平台和C/S结构,利用VC#语言和SQL Server数据库开发了变压器故障诊断系统。该系统主要包括变压器油色谱预测、变压器故障诊断以及数据仓库OLAP分析等几个模块。该系统已在河北衡水电力公司投入运行。
Power transformer is one of the expenxive device in power system, power transformer fault diagnose is vital to make the whole power system run normally, the failures of transformers can result in serious issues, such as service disruptions and severe economic losses.Transformer fault diagnose and fault prediction are the basis of the transformer condition based maintenance. In this paper we have done in-depth research in transformer data warehouse construction, transformer fault diagnosis and fault forecast.
A data precessing method is provided through constructing the power transformer fault information data warehouse, based on analyzing and reorganizing the various existing data sources, the transformer failure diagnosis and early failure predication can be benefit from OLAP (On-Line Analysis Processing) and data mining technology.
Aiming at the low diagnositic performance of single models caused by transformer testing data, a combination diagnosis model based on the Bayes classifiers and SVM is proposed in this paper. This model can solve the low efficientcy and accuracy when using support vector machines (SVM) to solve multi-classification question.
Due to the randomness and uncertainty of power transformer fault diagnosis data, a novel method using selective Bayes classifier SRBC is proposed to estimate probability distribution directly from incomplete transformer fault data, and then estimates mutually information. This proposed. SRBC approach can solve the randomness and uncertainty problem of power transformer fault diagnosis data. The experimental results show that this method can obviously enhance the accuracy rate.
Aiming at the low forecasting accuracy of traditional predictive approaches, a combinational model is proposed on the basis of SVM theory in this paper. During the process of the forecast, firstly several single forecast approaches are used to form a model group, and a set of data in time sequence on each dissolved gas are fitted by the model group. And then the fitted results by different traditional predictive models in time sequence act as the input of the support vector machine regression (SVMR) model, and the changeable weights combinational SVMR prediction model is obtained.
Adopting .NET platform and the C/S structure, the transformer fault diagnosis system has been developed using the VC# language and SQL Server. This system mainly includes the transformer oil chromatograph forecast, the transformer fault diagnosis as well as the data warehouse OLAP analysis and so on several modules. This system has been put the operation in the Hebei Hengshui electric power company.
为了进行变压器状态和故障自动预测,以及便于维护人员对于变压器状态进行人工预测,提出了构建电力变压器数据仓库的数据处理思想,在对有关变压器信息的多种数据源进行分析和重新组织的基础上,利用联机分析处理和数据挖掘技术,可对变压器故障诊断和早期故障预测起到积极作用。
针对单一诊断模型受变压器试验数据量和色谱数据随机因素影响较大的缺点,提出了以多个贝叶斯网络分类器作为故障诊断模型群和利用支持向量机进行集成的变压器故障组合诊断方法,该方法可解决支持向量机进行多分类问题时训练效率低、求解精度差的问题。
针对已有变压器故障诊断方法在信息缺失多、有偏差的情况下误判率高的问题,提出了利用选择性贝叶斯分类器SRBC直接从不完整的变压器试验数据估算变量的概率分,进而估算其互信息的方法,该方法可解决已有变压器故障诊断方法在信息缺失多、有偏差的情况下误判率高的问题。
针对单一预测模型的适应性较差的缺点,提出了一个基于支持向量机的变压器故障组合预测模型及其求解步骤。在预测过程中,首先利用多个单一预测方法构成预测模型群,对原始油中溶解气体数据进行拟合。然后,把预测模型群的拟合结果作为支持向量机回归模型的输入进行二次预测,形成变权重的组合预测。
采用.NET平台和C/S结构,利用VC#语言和SQL Server数据库开发了变压器故障诊断系统。该系统主要包括变压器油色谱预测、变压器故障诊断以及数据仓库OLAP分析等几个模块。该系统已在河北衡水电力公司投入运行。
Power transformer is one of the expenxive device in power system, power transformer fault diagnose is vital to make the whole power system run normally, the failures of transformers can result in serious issues, such as service disruptions and severe economic losses.Transformer fault diagnose and fault prediction are the basis of the transformer condition based maintenance. In this paper we have done in-depth research in transformer data warehouse construction, transformer fault diagnosis and fault forecast.
A data precessing method is provided through constructing the power transformer fault information data warehouse, based on analyzing and reorganizing the various existing data sources, the transformer failure diagnosis and early failure predication can be benefit from OLAP (On-Line Analysis Processing) and data mining technology.
Aiming at the low diagnositic performance of single models caused by transformer testing data, a combination diagnosis model based on the Bayes classifiers and SVM is proposed in this paper. This model can solve the low efficientcy and accuracy when using support vector machines (SVM) to solve multi-classification question.
Due to the randomness and uncertainty of power transformer fault diagnosis data, a novel method using selective Bayes classifier SRBC is proposed to estimate probability distribution directly from incomplete transformer fault data, and then estimates mutually information. This proposed. SRBC approach can solve the randomness and uncertainty problem of power transformer fault diagnosis data. The experimental results show that this method can obviously enhance the accuracy rate.
Aiming at the low forecasting accuracy of traditional predictive approaches, a combinational model is proposed on the basis of SVM theory in this paper. During the process of the forecast, firstly several single forecast approaches are used to form a model group, and a set of data in time sequence on each dissolved gas are fitted by the model group. And then the fitted results by different traditional predictive models in time sequence act as the input of the support vector machine regression (SVMR) model, and the changeable weights combinational SVMR prediction model is obtained.
Adopting .NET platform and the C/S structure, the transformer fault diagnosis system has been developed using the VC# language and SQL Server. This system mainly includes the transformer oil chromatograph forecast, the transformer fault diagnosis as well as the data warehouse OLAP analysis and so on several modules. This system has been put the operation in the Hebei Hengshui electric power company.
引文
[1]电力工业部安全监察及生产协调司[S].安全情况通报汇编.1994
[2] http://www.hbsafety.com.cn/article/225/438/200609/9014.shtml.
[3]许婧.电力设备状态检修技术研究综述[J].电网技术,2000,24(8):48～52
[4]王谦.基于模糊理论的电力变压器运行状态综合评估方法研究[D].重庆:重庆大学硕士学位论文.2005
[5]刘有为,李光范,高克利,等.制订《电气设备状态维修导则》的原则框架[J].电网技术,2003,27(6):64～67,76
[6]吴立增,朱永利. (2005)基于贝叶斯网络分类器的变压器综合故障诊断方法[J].电工技术学报, 2005, 4:49～55
[7]松浦虔士.电力设备の运行中绝缘诊断技术.日本电气学会技术报告.1992
[8] Y. -C Huang. Condition assessment of power transformers using genetic-based neural networks. IEE Science, Measurement and Technology, 2003, 150:19～24
[9] T.K. Saha, Zheng Tong Yao. Experience with return voltage measurements for assessing insulation conditions in service-aged transformers. IEEE Trans. Power Delivery, 2003, 18:128～135
[10] G.J. Paolett, M. Baier. Failure contributors of my electrical equipment and condition assessment program development. IEEE Trans. Industry Applications, 2002, 38:1668～1676
[11] Stephen M. Hess, William J. Biter, Stephen N. Hollingsworth. An evaluation method for application of condition-based maintenance technologies. 2001 Annual Reliability and Maintainability Symposium, 2001, 240～245
[12] G. Frimpong, T. Taylor. Developing an effective condition based maintenance program for substation equipment. 2003 Rural Electric Power Conf. , 2003. C6-1-C6-9
[13] J. E. Propst,P.T. Griffin. Replace, Refurbish, or Retain? Evaluating aging electrical systems and equipment. IEEE Trans. Industry Applications Magazine, 2002, 18:12～19
[14] W. H. Tang. An Evidential Reasoning Approach to Transformer Condition Assessments. IEEE Transactions on Power Delivery, 2004,99:1～9
[15] M. Darveniza. Investigations into effective methods for assessing the condition of insulation in aged power transformers. IEEE Transactions on Power Delivery, 1998,13:1214～1223
[16] IEC Publication 599. Interpretation for the Analysis of Gases in transformers and other Oil-Filled Electrical Equipment in Service[S].1978
[17]孙才新,陈伟根,李俭,等.电器设备油中气体在线监测与故障诊断技术[M].北京:科学出版社,2003
[18]高文胜,严璋,谈克雄.基于油中溶解气体分析的电力变压器绝缘故障诊断方法[J].电工电能新技术, 2000,19:22～26
[19]杜文霞,吕锋,句希源.基于BP神经网络的电力变压器故障诊断[J].变压器. 2007,44(3):45～47
[20]梁永春,李彦明.改进型组合RBF神经网络的变压器故障诊断[J].高电压技术. 2005, 31(9):31～33
[21]徐志钮,律方成.多神经网络方法在变压器油色谱故障诊断中的应用[J].高压电器. 2005, 41(3):206～208
[22]俞晓冬,马凤英,臧宏志.粗糙集理论与神经网络在变压器故障诊断中的应用[J].继电器. 2005, 34(1):10～15
[23]廖瑞金,廖玉祥,杨丽君,等.(2006)多神经网络与证据理论融合的变压器故障综合诊断方法的研究[J].中国电机工程学报, 26(3):119～124
[24]曹永刚,周玲,丁晓群,马文静.基于概率神经网络的电力变压器故障诊断[J].继电器. 2006, 34(3):9～12
[25]李天云,程思勇,童建东,王鹏宇.基于Elman神经网络的油浸式电力变压器故障诊断[J].继电器. 2006, 39(11):55～57.
[26]段慧达,王建南,白晶.基于动态对角递归网络的变压器故障诊断[J].微计算机信息,2006,22(1):6～7,38
[27] Wenqing Zhao, Yongli Zhu. Rough Set Theory for Fault Diganose for Data Mining on Power Transformer. IEEE TENCON 2006, Hongkong, China, November, 2006,1265～1268
[28]谢可夫,罗安.遗传算法在变压器故障诊断中的应用[J].电力自动化设备. 2005,25:55～58
[29] Pedro Vicente Jover,Rodríguez,Antero Arkkio. Detection of stator winding fault in induction motor using fuzzy logic. Applied Soft Computing,2008,8(2): 1112～1120
[30] Lixin Dong, Dengming Xiao, Yishan Liang and Yilu Liu. Rough set and fuzzy wavelet neural network integrated with least square weighted fusion algorithm based fault diagnosis research for power transformers. Electric Power Systems Research,2008,78(1):129～136
[31]熊浩,张晓星,廖瑞金,等.基于动态聚类的电力变压器故障诊断[J].仪器仪表学报, 2007,28(3):456～459
[32]王楠,律方成,刘云鹏,等.基于粗糙集理论与模糊Petri网络的油浸电力变压器综合故障诊断[J].2003,中国电机工程学报, 23:127～132
[33]宋斌,于萍,罗运柏,文习山.基于灰关联熵的充油变压器故障诊断方法[J].电力系统自动化, 2005,29(18):76～79
[34]董明,严璋,杨莉,等.基于证据推理的电力变压器故障诊断策略[J].中国电机工程学报, 2005,26(1):106～114
[35]陈江波,付锡年,聂德鑫,等. UHV变压器故障监测与诊断的信息决策平台[J].高电压技术, 2006, 32(12):108～111
[36]王志勇.基于粗糙集与模糊规则的变压器故障诊断方法[J].继电器, 2006, 34(9): 8～11
[37]周平.人工智能专家系统在变压器故障诊断中的应用研究[D].中国电力科学研究院博士学位论文.1992
[38]张鸣柳.模糊数学在电力变压器内部故障诊断中的应用研究[D].重庆大学硕士学位论文.1997
[39]徐文,王大忠等.基于模糊理论的变压器故障诊断专家系统[J].电力系统自动化.1995,19(6):32～37
[40]何跃英,江荣汉.基于模糊理论的电力设备故障诊断专家系统[J].电工技术学报.1994,9(1):43～46
[41] Hong-Tzer Yang, Chiung-Chou Liao, Jeng-Hong Chou. Fuzzy Learning Vector Quantization Networks for Power Transformer Condition Assessment. IEEE Transactions on Dielectrics and Electrical Insulation, 2001, 8(1): 143～149
[42]杨莉,尚勇,周跃峰,严璋.基于概率推理和模糊数学的变压器综合故障诊断模型[J].中国电机工程学报, 2000, 20(7): 19～23
[43]李延沐,袁鹏,牟磊,李彦明.基于自适应神经模糊推理系统(ANFIS)的变压器超高频局部放电模式识别.电工电能新技术, 2005, 24(4):30～33
[44]李明华,屈彦明,周孟戈,董明,严璋.基于多Agent及Petri网的变压器故障诊断系统[J].西安交通大学学报, 2006, 40(2):223～227
[45] Wu Lizeng, Zhu, Yongli, Li Xueyu. Application of multi-agent and data mining techniques in condition assessment of transformers. POWERCON 2004, 823～827
[46]项新建.基于粗糙集理论的变压器故障诊断专家系统研究[J].仪器仪表学报, 2005, 26(1):93～96
[47]陈新岗,李太福.基于DGA特征量的变压器绝缘故障诊断专家系统的研究[J].变压器. 2005,42(1):33～36
[48]廖瑞金.变压器绝缘故障诊断黑板型专家系统和基于遗传算法的故障预测研究[D].重庆大学,2003
[49] Wenqing Zhao, Yongli Zhu. A Fault Diagnosis Model for Power Transformer Based on Statistical Theory. Proceedings of 2007 International Conference on Wavelet Analysis and Pattern Recognition, Beijing,China,2007,962～966
[50]董明,孟源源,徐长响,严璋.基于支持向量机及油中溶解气体分析的大型电力变压器故障诊断模型研究[J].中国电机工程学报, 2003,23(7):88～92
[51]肖燕彩,陈秀海,朱衡君.遗传支持向量机在电力变压器故障诊断中的应用[J].上海交通大学学报,2007,41(11):1878～1881
[52] Nir Friedman and Moises Goldszmidt. Building classifiers using Bayesian networks. In AAAI/IAAI, 1996,2:1277～1284
[53] Clark, P. & Niblett, T. (1989). The CN2 Induction Algorithm. Machine Learning, 1989, 3(4): 261～283
[54] Kononenko. I. Semi-naive Bayesian classifier. In: Proceedings of the Sixth European Working Session on Learning, Springer-Verlag, 1991, 206～219
[55] Pazzani, M. J. Searching for Dependencies in Bayesian Classifiers. Learning from Data: Artificial intelligence And Statistics V. New York, NY: Springer-Verlag, 1996,239～248
[56] Nir Friedman, Dan Geiger, and Moises Goldszmidt. Bayesian network classifiers. Machine Learning, 1997,29(2-3):131～163
[57] Sahami, M. Learning Limited Dependence Bayesian Classifiers. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. Portland, OR: AAAI Press, 1996,335～338
[58] Jie Cheng and Russell Greiner. Learning Bayesian belief network classifiers: Algorithms and system. In Canadian Conference on AI, 2001,141～151
[59] Vapnik V. Statistical Learning Theory[M]. New York: Springer,1998
[60] Guo-rui Ji. Wind Speed Forecasting Based on Support Vector Machine with Forecasting Erroe Estimation. Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 2007,2735～2739
[61] Yong-jie Zhai, Xue-wu Dai, Qian Zhou. Detection for Gas Turbines Based on Long-Term Prediction using Self-Organizing Fuzzy Neural Networks. Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 2007,1120～1125
[62]孙才新,陈伟根,李俭,等.电器设备油中气体在线监测与故障诊断技术[M].北京:科学出版社,2003,239～241
[63]孙才新,毕为民,周湶,等.灰色预测参数模型新模式及其在电气绝缘故障预测中的应用[J].控制理论与应用,2003,20(5):797～801
[64]苏勋家,王汉功.灰色理论在故障预测中的应用[J].中国设备管理, 1991(12):20～23
[65]龙文明.灰色理论在电炉机械设备故障预测中的应用[J].工业加热,1993(3):21～23
[66]李勇,曹祖庆.汽轮发电机组故障预测方法[J].汽轮机技术,1996,38(1):17～20
[67]王清晓,陈家锭.灰色马尔柯夫链方法在设备故障预测中的应用初探[J].机械科学与技术,1997,16(3):491～495
[68]程惠涛,黄文虎.基于灰色模型的故障预报技术及其在空间推进系统上的应用[J].推进技术,1998,19(3):74～77
[69]李勇,孙艳萍.用于故障预测的BP网络模型及改进[J].东北电力学院学报,1999,19(1):27～32
[70]黄景德,王兴贵.故障模糊预测系统推理机制研究[J].计算机工程,2000,26(11):63～64
[71]黄景德,崔山宝,王兴贵,等.正向推理型故障模糊预测系统的知识表示与推理[J].计算机工程,2001,27(2):78～79
[72]蒋瑜,陈循,罗护,等.基于神经网络的智能故障诊断技术研究综述[J].设备管理与维修,2001(3):28～30
[73]黄景德,王兴贵,王祖光,等.动态模糊综合评判法及其在故障预测中的应用[J].模糊系统与数学,2001,15(4):96～99
[74] Mokhnacke, L. Boubakeur, A. Feliachi, A. Prediction of thermal ageing in transformer oil and high voltage PVC cables using artificial neural networks. Science, Measurement and Technology, IEE Proceedings, 2003, 150(3): 107～112
[75] Marino, P., Sigiienza, C., Poza, F. etl supporting information system for power transformer fault forecasting applications. Industrial Electronics Society[C].The 29th Annual Conference of the IEEE, 2003,2: 1899～1904
[76]陈丽,栗然.用于变压器故障预测的决策树—蒙特卡洛方法[J].华北电力大学学报, 2006, 33(2):12～15
[77]王鹏,许涛.用统计学习理论预测变压器油中溶解气体浓度[J].高电压技术, 2003, 29 (11):13～14
[78]吴立增.变压器状态评估方法的研究[D].河北,华北电力大学,2005
[79]周利军,吴广宁,宿冲.变压器油中故障气体的复合预测方法[J].西南交通大学学报, 2006, 41(2):150～153
[80] Inmon W H.数据仓库(Building the data warehouse) [M].北京:机械工业出版社(China Machine Press),2000
[81] Rodriguez-Ortiz G.Using data mining techniques to classify energy demand[J]. International Journal of Power and Energy Systems, 1999, 19(2): 56～66
[82]麻秀范,张粒子.基于数据仓库的经济活动分析决策支持系统[J].电力自动化设备, 2003, 22(12): 23～26
[83]邓乃扬,田英杰.数据挖掘中的新方法——支持向量机[M].北京:科学出版社,2004
[84]柳回春,马树元.支持向量机的研究现状[J].中国图像图形学报, 2002,7(6):618～62
[85] Vapnik V. The nature of statistical learning theory[M]. New York;Springer, 2000
[86] Knerr S, Personnaz L, Dreyfus G.. Single-layer learning revisited: A stepwise procedure for building and training a neural network[J]. Architectures and Applications, New York: Springer-Verlag,1990
[87] Platt .J. Large margin DAGs for multiclass classification[J]. neural information. 2000, 16(2): 246～252
[88]唐发明,王仲东,陈绵云.一种新的二叉树多类支持向量机算法[J].计算机工程与应用,2005,15(7):23～26
[89] Stephenson T A. An Introduction To Bayesian Network Theory And Usage. IDIAP-RR 00-03, Feb, 2000
[90] Kim J H, Pearl J. A Computation Model for Causal and Diagnostic Reasoning in Inference Systems. Proceedings of the 8th International Joint Conference on AI, Los Angeles, 1983, 190～193
[91] Jensen FV. An Introduction to Bayesian Networks. UCL Press Ltd. London, 1996
[92] Charniak E. Bayesian Networks without Tears. AI Magazine, 1991, 12(4):50～63
[93] Heckerman D, Shachter R. Decision-Theoretic Foundations for Causal Reasoning. Journalof Artificial Intelligence Research, 1995, 3:405～430
[94] Cowell R G, Dawid A P, Lauritzen S L, Spiegelhalter M R C. Probabilistic Networks and Expert Systems. Springer-Verlag, 1999
[95] Jensen F V. Bayesian Network Basics. AISB Quarterly, 1996, 94:9～22
[96] Bresnick TA, Buede D M, Tatman J A. Introduction to Bayesian Networks: A Tutorial for the 66th MORS Symposium. Naval Postgraduate School Monterey, California, 23-25 June 1998
[97] Pearl J. Graphical Models for Probabilistic and Causal Reasoning. The Computer Science and Engineering Handbook, Kluwer Academic Publishers, 1997:697～714
[98] Haddawy P. An Overview of Some Recent Developments in Bayesian Problem Solving Techniques. AI Magazine, 1999, 20(l):23～36
[99] Neapolitan R E. Probabilistic Reasoning in Expert Systems: Theory and Algorithms. Wiley, New York, 1990
[100] Pearl J. Fusion, Propagation, and Structuring in Belief Networks. Artificial Intelligence, 1986, 29(9):241～288
[101] Shachter R D. Probabilistic Inference and Influence Diagrams. Operations Research, 1988, 36: 589～605
[102] Heckerman D, Geiger D, Chickering M. Learning Bayesian networks: The combination of knowledge and statistical data [J]. Machine Learning, 1995, 20:197～243.
[103]邢永康,沈一栋.基于互信息和测度学习信度网结构.重庆大学学报(自然科学版), 2001, 24(1): 78～83
[104] Cooper, G. & Herskovits, E. A Bayesian method for the induction of probailistic networks form data. Machine Learning 9:309～347
[105] LAM W, BACHUS F. Learning Bayesian Networks: An Approach based on the MDL principle [J]. Computation intelligence, 1994, 10:269～293.
[106] Jaroslaw P. Sacha, Lucy S. Goodenday, Krzysztof J. Cios. Bayesian learning for cardiac SPECT image interpretation [J]. Artificial Intelligence in Medicine, 2002, 26(1-2), 109～143
[107]眭俊明,姜远,周志华.基于频繁项集挖掘的贝叶斯分类算法[J].计算机研究与发展, 2007,44(8):1293～1300
[108] Chow C K, Liu C N. Approximation discrete probability distributions with dependence trees[J]. IEEE Trans. on Information Theory. 1968,14:462～467
[109] M.Wang, A.J. Vandermaar, K. D. Srivastava. Review of condition assessment of power transformers in service. IEEE Electrical Insulation Magazine,2002,18(6):12～25
[110]朱永利,吴立增.贝叶斯分类器与粗糙集相结合的变压器综合故障诊断[J].中国电机工程学报, 2005,45(10):161～167
[111] Kwang, I.K.. Texture-based Approach for Text Dectection in Images Using Support Vector Machines and Continuously Adaptive Mean Shift Algorithm [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2003,25(2):1631～1639
[112] Jin-an G. Evoked Potentials Estimation in Brain-Computer Interface Using Support Vector Machine[C]. In Chongqing Conference on Rough Set and Knowledge Technology, 2006,701～707
[113] Zheng,R.Y., Chou, K., Bio-support vector machine for computational proteomics [J]. Bioinformnatics. 2004,20:735～741
[114] Pengjun C., Guoying W. Facial Expression Recognition on Rough Set Theory and SVM [C]. In Chongqing Conference on Rough Set and Knowledge Technology, 2006, 772～778
[115] Chickering D M, Heckerman D, M. Large sample learning of Bayesian networks is NP-Hard[J]. Journal of Machine Learning Research. 2004,39(5):1287～1330
[116] P., C. and S. J. Bayesian classification (AutoClass): Theory and results. In F. U., P.-S. G., S. P., and U. R. (Eds.), Advances in Knowledge Discovery and Data Mining, 1995, 153～180
[117] Friedman N. The Bayesian structural EM algorithm. In: Cooper GF, Moral S eds. Proceedings of the 14th Conf. on Uncertainty in Artificial Intelligence, San Francisco: Morgan Kaufmann, 1998,129～138
[118] J. Friedman. Another Approach to Polychotomous Classification. Dept. Statistic, Stanford Univ., Stanford, CA. 1996.
[119]李俭.大型电力变压器以油中溶解气体为特征量的内部故障诊断模型研究[D].重庆,重庆大学大学, 2001.
[120]彭宁云.基于DGA技术的变压器故障智能诊断系统研究[D].湖北,武汉大学, 2004.
[121]林茂六,陈春雨.基于傅立叶核与径向基核的支持向量机性能之比较[J].重庆邮电学院学报,2005,17(6):647～650
[122]陈景年,黄厚宽,田凤占,等.用于不完整数据的选择性贝叶斯分类器[J].计算机研究与发展, 2007, 44(8):1324～1329
[123] R J A Little, D B Rubin. Statistical Analysis with Missing Data [M]. Wiley, New York, 1987.
[124] D J Spiegelhalter, R G Cowell. Learning in probabilistic expert systems[C]. In: J Bernardo, J Berger, A P Dawid, A F M Smith (Eds.), Bayesian Statistics 4, Oxford University Press, Oxford, UK, 1992. 447– 466.
[125] M Ramoni, P Sebastiani. Robust Bayes classifiers [J]. Artificial Intelligence, 2001, 125: 209～226
[126] P H Winston. Artificial intelligence [M]. MA: Addison- Wesley, 1992
[127]王有元,廖瑞金,孙才新,等.变压器油中溶解气体浓度灰色预测模型的改进[J].高电压技术,2003,29(4):24～26
[128]袁保奎,郭基伟,唐国庆.应用灰色理论预测变压器等充油设备内的油中气体浓度[J].电力系统及其自动化学报,2001,13(3):40～42
[129]罗运拍,于萍,宋斌,等.用灰色模型预测变压器油中溶解气体的含量[J].中国电机工程学报,2001,21(3):65～69
[130]王晶,刘建新.基于灰色新预测模式的变压器故障预测[J].华北电力大学学报,2007,34(1):10～14
[131] Bates J M,Granger C W J.Combination of forecasts[J].Journal of Operational Research Quarterly, 1969,(20):451～468
[132] Chen K Y,Wang C H.Support vector regression with genetic algorithms in forecasting tourism demand[J].Tourism Manage,2007,2 (1):215～226
[133] Lai J.B,Wang H.Q,Zhu L..Study of network security situation awareness model based on simple additive weight and grey theory[C]. International Conference on Computational Intelligence and Security,Guangzhou, China:Guangdong University of Technology,2006,1545～1548
[134] Pai P F.System reliability forecasting by support vector machines with genetic algorithms [J]. Mathematics Computer Model,2006,4(3):262～274
[135]傅立.灰色系统理论及其应用[M].北京:科学技术文献出版社,1991,83～85
[136] Liu Sifeng, Lin Yi. An Introduction to Grey System: Foundations Methodology's and Applications [M]. Slippery Rock: IIGSS Academic Publisher,1998
[137]刘琼,刘琳,刘全.用最优化灰色理论进行电力负荷预测的研究.东北电力技术,1998,30(3):17～21
[138]杨大地,涂光裕编.数值分析[M].重庆:重庆大学出版社,1998
[139]唐小我.组合预测误差信息矩阵研究[J].电子科技大学学报,1992,21(4):448～454
[140]马永开,唐小我,杨桂元.非负权重最优组合预测方法的基本理论研究[J].运筹与管理,1997,6(2):1～8
[141]陈华友,侯定丕.基于预测有效度的优性组合预测模型的研究[J].中国科学技术大学学报,2002,32(2):172～180
[142]王应明.基于相关性的组合预测方法研究[J].预测,2002,21(2):58～62
[143]林茂六,陈春雨.基于傅立叶核与径向基核的支持向量机性能之比较[J].重庆邮电学院学报,2005,17(6):647～650
[2] http://www.hbsafety.com.cn/article/225/438/200609/9014.shtml.
[3]许婧.电力设备状态检修技术研究综述[J].电网技术,2000,24(8):48～52
[4]王谦.基于模糊理论的电力变压器运行状态综合评估方法研究[D].重庆:重庆大学硕士学位论文.2005
[5]刘有为,李光范,高克利,等.制订《电气设备状态维修导则》的原则框架[J].电网技术,2003,27(6):64～67,76
[6]吴立增,朱永利. (2005)基于贝叶斯网络分类器的变压器综合故障诊断方法[J].电工技术学报, 2005, 4:49～55
[7]松浦虔士.电力设备の运行中绝缘诊断技术.日本电气学会技术报告.1992
[8] Y. -C Huang. Condition assessment of power transformers using genetic-based neural networks. IEE Science, Measurement and Technology, 2003, 150:19～24
[9] T.K. Saha, Zheng Tong Yao. Experience with return voltage measurements for assessing insulation conditions in service-aged transformers. IEEE Trans. Power Delivery, 2003, 18:128～135
[10] G.J. Paolett, M. Baier. Failure contributors of my electrical equipment and condition assessment program development. IEEE Trans. Industry Applications, 2002, 38:1668～1676
[11] Stephen M. Hess, William J. Biter, Stephen N. Hollingsworth. An evaluation method for application of condition-based maintenance technologies. 2001 Annual Reliability and Maintainability Symposium, 2001, 240～245
[12] G. Frimpong, T. Taylor. Developing an effective condition based maintenance program for substation equipment. 2003 Rural Electric Power Conf. , 2003. C6-1-C6-9
[13] J. E. Propst,P.T. Griffin. Replace, Refurbish, or Retain? Evaluating aging electrical systems and equipment. IEEE Trans. Industry Applications Magazine, 2002, 18:12～19
[14] W. H. Tang. An Evidential Reasoning Approach to Transformer Condition Assessments. IEEE Transactions on Power Delivery, 2004,99:1～9
[15] M. Darveniza. Investigations into effective methods for assessing the condition of insulation in aged power transformers. IEEE Transactions on Power Delivery, 1998,13:1214～1223
[16] IEC Publication 599. Interpretation for the Analysis of Gases in transformers and other Oil-Filled Electrical Equipment in Service[S].1978
[17]孙才新,陈伟根,李俭,等.电器设备油中气体在线监测与故障诊断技术[M].北京:科学出版社,2003
[18]高文胜,严璋,谈克雄.基于油中溶解气体分析的电力变压器绝缘故障诊断方法[J].电工电能新技术, 2000,19:22～26
[19]杜文霞,吕锋,句希源.基于BP神经网络的电力变压器故障诊断[J].变压器. 2007,44(3):45～47
[20]梁永春,李彦明.改进型组合RBF神经网络的变压器故障诊断[J].高电压技术. 2005, 31(9):31～33
[21]徐志钮,律方成.多神经网络方法在变压器油色谱故障诊断中的应用[J].高压电器. 2005, 41(3):206～208
[22]俞晓冬,马凤英,臧宏志.粗糙集理论与神经网络在变压器故障诊断中的应用[J].继电器. 2005, 34(1):10～15
[23]廖瑞金,廖玉祥,杨丽君,等.(2006)多神经网络与证据理论融合的变压器故障综合诊断方法的研究[J].中国电机工程学报, 26(3):119～124
[24]曹永刚,周玲,丁晓群,马文静.基于概率神经网络的电力变压器故障诊断[J].继电器. 2006, 34(3):9～12
[25]李天云,程思勇,童建东,王鹏宇.基于Elman神经网络的油浸式电力变压器故障诊断[J].继电器. 2006, 39(11):55～57.
[26]段慧达,王建南,白晶.基于动态对角递归网络的变压器故障诊断[J].微计算机信息,2006,22(1):6～7,38
[27] Wenqing Zhao, Yongli Zhu. Rough Set Theory for Fault Diganose for Data Mining on Power Transformer. IEEE TENCON 2006, Hongkong, China, November, 2006,1265～1268
[28]谢可夫,罗安.遗传算法在变压器故障诊断中的应用[J].电力自动化设备. 2005,25:55～58
[29] Pedro Vicente Jover,Rodríguez,Antero Arkkio. Detection of stator winding fault in induction motor using fuzzy logic. Applied Soft Computing,2008,8(2): 1112～1120
[30] Lixin Dong, Dengming Xiao, Yishan Liang and Yilu Liu. Rough set and fuzzy wavelet neural network integrated with least square weighted fusion algorithm based fault diagnosis research for power transformers. Electric Power Systems Research,2008,78(1):129～136
[31]熊浩,张晓星,廖瑞金,等.基于动态聚类的电力变压器故障诊断[J].仪器仪表学报, 2007,28(3):456～459
[32]王楠,律方成,刘云鹏,等.基于粗糙集理论与模糊Petri网络的油浸电力变压器综合故障诊断[J].2003,中国电机工程学报, 23:127～132
[33]宋斌,于萍,罗运柏,文习山.基于灰关联熵的充油变压器故障诊断方法[J].电力系统自动化, 2005,29(18):76～79
[34]董明,严璋,杨莉,等.基于证据推理的电力变压器故障诊断策略[J].中国电机工程学报, 2005,26(1):106～114
[35]陈江波,付锡年,聂德鑫,等. UHV变压器故障监测与诊断的信息决策平台[J].高电压技术, 2006, 32(12):108～111
[36]王志勇.基于粗糙集与模糊规则的变压器故障诊断方法[J].继电器, 2006, 34(9): 8～11
[37]周平.人工智能专家系统在变压器故障诊断中的应用研究[D].中国电力科学研究院博士学位论文.1992
[38]张鸣柳.模糊数学在电力变压器内部故障诊断中的应用研究[D].重庆大学硕士学位论文.1997
[39]徐文,王大忠等.基于模糊理论的变压器故障诊断专家系统[J].电力系统自动化.1995,19(6):32～37
[40]何跃英,江荣汉.基于模糊理论的电力设备故障诊断专家系统[J].电工技术学报.1994,9(1):43～46
[41] Hong-Tzer Yang, Chiung-Chou Liao, Jeng-Hong Chou. Fuzzy Learning Vector Quantization Networks for Power Transformer Condition Assessment. IEEE Transactions on Dielectrics and Electrical Insulation, 2001, 8(1): 143～149
[42]杨莉,尚勇,周跃峰,严璋.基于概率推理和模糊数学的变压器综合故障诊断模型[J].中国电机工程学报, 2000, 20(7): 19～23
[43]李延沐,袁鹏,牟磊,李彦明.基于自适应神经模糊推理系统(ANFIS)的变压器超高频局部放电模式识别.电工电能新技术, 2005, 24(4):30～33
[44]李明华,屈彦明,周孟戈,董明,严璋.基于多Agent及Petri网的变压器故障诊断系统[J].西安交通大学学报, 2006, 40(2):223～227
[45] Wu Lizeng, Zhu, Yongli, Li Xueyu. Application of multi-agent and data mining techniques in condition assessment of transformers. POWERCON 2004, 823～827
[46]项新建.基于粗糙集理论的变压器故障诊断专家系统研究[J].仪器仪表学报, 2005, 26(1):93～96
[47]陈新岗,李太福.基于DGA特征量的变压器绝缘故障诊断专家系统的研究[J].变压器. 2005,42(1):33～36
[48]廖瑞金.变压器绝缘故障诊断黑板型专家系统和基于遗传算法的故障预测研究[D].重庆大学,2003
[49] Wenqing Zhao, Yongli Zhu. A Fault Diagnosis Model for Power Transformer Based on Statistical Theory. Proceedings of 2007 International Conference on Wavelet Analysis and Pattern Recognition, Beijing,China,2007,962～966
[50]董明,孟源源,徐长响,严璋.基于支持向量机及油中溶解气体分析的大型电力变压器故障诊断模型研究[J].中国电机工程学报, 2003,23(7):88～92
[51]肖燕彩,陈秀海,朱衡君.遗传支持向量机在电力变压器故障诊断中的应用[J].上海交通大学学报,2007,41(11):1878～1881
[52] Nir Friedman and Moises Goldszmidt. Building classifiers using Bayesian networks. In AAAI/IAAI, 1996,2:1277～1284
[53] Clark, P. & Niblett, T. (1989). The CN2 Induction Algorithm. Machine Learning, 1989, 3(4): 261～283
[54] Kononenko. I. Semi-naive Bayesian classifier. In: Proceedings of the Sixth European Working Session on Learning, Springer-Verlag, 1991, 206～219
[55] Pazzani, M. J. Searching for Dependencies in Bayesian Classifiers. Learning from Data: Artificial intelligence And Statistics V. New York, NY: Springer-Verlag, 1996,239～248
[56] Nir Friedman, Dan Geiger, and Moises Goldszmidt. Bayesian network classifiers. Machine Learning, 1997,29(2-3):131～163
[57] Sahami, M. Learning Limited Dependence Bayesian Classifiers. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. Portland, OR: AAAI Press, 1996,335～338
[58] Jie Cheng and Russell Greiner. Learning Bayesian belief network classifiers: Algorithms and system. In Canadian Conference on AI, 2001,141～151
[59] Vapnik V. Statistical Learning Theory[M]. New York: Springer,1998
[60] Guo-rui Ji. Wind Speed Forecasting Based on Support Vector Machine with Forecasting Erroe Estimation. Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 2007,2735～2739
[61] Yong-jie Zhai, Xue-wu Dai, Qian Zhou. Detection for Gas Turbines Based on Long-Term Prediction using Self-Organizing Fuzzy Neural Networks. Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 2007,1120～1125
[62]孙才新,陈伟根,李俭,等.电器设备油中气体在线监测与故障诊断技术[M].北京:科学出版社,2003,239～241
[63]孙才新,毕为民,周湶,等.灰色预测参数模型新模式及其在电气绝缘故障预测中的应用[J].控制理论与应用,2003,20(5):797～801
[64]苏勋家,王汉功.灰色理论在故障预测中的应用[J].中国设备管理, 1991(12):20～23
[65]龙文明.灰色理论在电炉机械设备故障预测中的应用[J].工业加热,1993(3):21～23
[66]李勇,曹祖庆.汽轮发电机组故障预测方法[J].汽轮机技术,1996,38(1):17～20
[67]王清晓,陈家锭.灰色马尔柯夫链方法在设备故障预测中的应用初探[J].机械科学与技术,1997,16(3):491～495
[68]程惠涛,黄文虎.基于灰色模型的故障预报技术及其在空间推进系统上的应用[J].推进技术,1998,19(3):74～77
[69]李勇,孙艳萍.用于故障预测的BP网络模型及改进[J].东北电力学院学报,1999,19(1):27～32
[70]黄景德,王兴贵.故障模糊预测系统推理机制研究[J].计算机工程,2000,26(11):63～64
[71]黄景德,崔山宝,王兴贵,等.正向推理型故障模糊预测系统的知识表示与推理[J].计算机工程,2001,27(2):78～79
[72]蒋瑜,陈循,罗护,等.基于神经网络的智能故障诊断技术研究综述[J].设备管理与维修,2001(3):28～30
[73]黄景德,王兴贵,王祖光,等.动态模糊综合评判法及其在故障预测中的应用[J].模糊系统与数学,2001,15(4):96～99
[74] Mokhnacke, L. Boubakeur, A. Feliachi, A. Prediction of thermal ageing in transformer oil and high voltage PVC cables using artificial neural networks. Science, Measurement and Technology, IEE Proceedings, 2003, 150(3): 107～112
[75] Marino, P., Sigiienza, C., Poza, F. etl supporting information system for power transformer fault forecasting applications. Industrial Electronics Society[C].The 29th Annual Conference of the IEEE, 2003,2: 1899～1904
[76]陈丽,栗然.用于变压器故障预测的决策树—蒙特卡洛方法[J].华北电力大学学报, 2006, 33(2):12～15
[77]王鹏,许涛.用统计学习理论预测变压器油中溶解气体浓度[J].高电压技术, 2003, 29 (11):13～14
[78]吴立增.变压器状态评估方法的研究[D].河北,华北电力大学,2005
[79]周利军,吴广宁,宿冲.变压器油中故障气体的复合预测方法[J].西南交通大学学报, 2006, 41(2):150～153
[80] Inmon W H.数据仓库(Building the data warehouse) [M].北京:机械工业出版社(China Machine Press),2000
[81] Rodriguez-Ortiz G.Using data mining techniques to classify energy demand[J]. International Journal of Power and Energy Systems, 1999, 19(2): 56～66
[82]麻秀范,张粒子.基于数据仓库的经济活动分析决策支持系统[J].电力自动化设备, 2003, 22(12): 23～26
[83]邓乃扬,田英杰.数据挖掘中的新方法——支持向量机[M].北京:科学出版社,2004
[84]柳回春,马树元.支持向量机的研究现状[J].中国图像图形学报, 2002,7(6):618～62
[85] Vapnik V. The nature of statistical learning theory[M]. New York;Springer, 2000
[86] Knerr S, Personnaz L, Dreyfus G.. Single-layer learning revisited: A stepwise procedure for building and training a neural network[J]. Architectures and Applications, New York: Springer-Verlag,1990
[87] Platt .J. Large margin DAGs for multiclass classification[J]. neural information. 2000, 16(2): 246～252
[88]唐发明,王仲东,陈绵云.一种新的二叉树多类支持向量机算法[J].计算机工程与应用,2005,15(7):23～26
[89] Stephenson T A. An Introduction To Bayesian Network Theory And Usage. IDIAP-RR 00-03, Feb, 2000
[90] Kim J H, Pearl J. A Computation Model for Causal and Diagnostic Reasoning in Inference Systems. Proceedings of the 8th International Joint Conference on AI, Los Angeles, 1983, 190～193
[91] Jensen FV. An Introduction to Bayesian Networks. UCL Press Ltd. London, 1996
[92] Charniak E. Bayesian Networks without Tears. AI Magazine, 1991, 12(4):50～63
[93] Heckerman D, Shachter R. Decision-Theoretic Foundations for Causal Reasoning. Journalof Artificial Intelligence Research, 1995, 3:405～430
[94] Cowell R G, Dawid A P, Lauritzen S L, Spiegelhalter M R C. Probabilistic Networks and Expert Systems. Springer-Verlag, 1999
[95] Jensen F V. Bayesian Network Basics. AISB Quarterly, 1996, 94:9～22
[96] Bresnick TA, Buede D M, Tatman J A. Introduction to Bayesian Networks: A Tutorial for the 66th MORS Symposium. Naval Postgraduate School Monterey, California, 23-25 June 1998
[97] Pearl J. Graphical Models for Probabilistic and Causal Reasoning. The Computer Science and Engineering Handbook, Kluwer Academic Publishers, 1997:697～714
[98] Haddawy P. An Overview of Some Recent Developments in Bayesian Problem Solving Techniques. AI Magazine, 1999, 20(l):23～36
[99] Neapolitan R E. Probabilistic Reasoning in Expert Systems: Theory and Algorithms. Wiley, New York, 1990
[100] Pearl J. Fusion, Propagation, and Structuring in Belief Networks. Artificial Intelligence, 1986, 29(9):241～288
[101] Shachter R D. Probabilistic Inference and Influence Diagrams. Operations Research, 1988, 36: 589～605
[102] Heckerman D, Geiger D, Chickering M. Learning Bayesian networks: The combination of knowledge and statistical data [J]. Machine Learning, 1995, 20:197～243.
[103]邢永康,沈一栋.基于互信息和测度学习信度网结构.重庆大学学报(自然科学版), 2001, 24(1): 78～83
[104] Cooper, G. & Herskovits, E. A Bayesian method for the induction of probailistic networks form data. Machine Learning 9:309～347
[105] LAM W, BACHUS F. Learning Bayesian Networks: An Approach based on the MDL principle [J]. Computation intelligence, 1994, 10:269～293.
[106] Jaroslaw P. Sacha, Lucy S. Goodenday, Krzysztof J. Cios. Bayesian learning for cardiac SPECT image interpretation [J]. Artificial Intelligence in Medicine, 2002, 26(1-2), 109～143
[107]眭俊明,姜远,周志华.基于频繁项集挖掘的贝叶斯分类算法[J].计算机研究与发展, 2007,44(8):1293～1300
[108] Chow C K, Liu C N. Approximation discrete probability distributions with dependence trees[J]. IEEE Trans. on Information Theory. 1968,14:462～467
[109] M.Wang, A.J. Vandermaar, K. D. Srivastava. Review of condition assessment of power transformers in service. IEEE Electrical Insulation Magazine,2002,18(6):12～25
[110]朱永利,吴立增.贝叶斯分类器与粗糙集相结合的变压器综合故障诊断[J].中国电机工程学报, 2005,45(10):161～167
[111] Kwang, I.K.. Texture-based Approach for Text Dectection in Images Using Support Vector Machines and Continuously Adaptive Mean Shift Algorithm [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2003,25(2):1631～1639
[112] Jin-an G. Evoked Potentials Estimation in Brain-Computer Interface Using Support Vector Machine[C]. In Chongqing Conference on Rough Set and Knowledge Technology, 2006,701～707
[113] Zheng,R.Y., Chou, K., Bio-support vector machine for computational proteomics [J]. Bioinformnatics. 2004,20:735～741
[114] Pengjun C., Guoying W. Facial Expression Recognition on Rough Set Theory and SVM [C]. In Chongqing Conference on Rough Set and Knowledge Technology, 2006, 772～778
[115] Chickering D M, Heckerman D, M. Large sample learning of Bayesian networks is NP-Hard[J]. Journal of Machine Learning Research. 2004,39(5):1287～1330
[116] P., C. and S. J. Bayesian classification (AutoClass): Theory and results. In F. U., P.-S. G., S. P., and U. R. (Eds.), Advances in Knowledge Discovery and Data Mining, 1995, 153～180
[117] Friedman N. The Bayesian structural EM algorithm. In: Cooper GF, Moral S eds. Proceedings of the 14th Conf. on Uncertainty in Artificial Intelligence, San Francisco: Morgan Kaufmann, 1998,129～138
[118] J. Friedman. Another Approach to Polychotomous Classification. Dept. Statistic, Stanford Univ., Stanford, CA. 1996.
[119]李俭.大型电力变压器以油中溶解气体为特征量的内部故障诊断模型研究[D].重庆,重庆大学大学, 2001.
[120]彭宁云.基于DGA技术的变压器故障智能诊断系统研究[D].湖北,武汉大学, 2004.
[121]林茂六,陈春雨.基于傅立叶核与径向基核的支持向量机性能之比较[J].重庆邮电学院学报,2005,17(6):647～650
[122]陈景年,黄厚宽,田凤占,等.用于不完整数据的选择性贝叶斯分类器[J].计算机研究与发展, 2007, 44(8):1324～1329
[123] R J A Little, D B Rubin. Statistical Analysis with Missing Data [M]. Wiley, New York, 1987.
[124] D J Spiegelhalter, R G Cowell. Learning in probabilistic expert systems[C]. In: J Bernardo, J Berger, A P Dawid, A F M Smith (Eds.), Bayesian Statistics 4, Oxford University Press, Oxford, UK, 1992. 447– 466.
[125] M Ramoni, P Sebastiani. Robust Bayes classifiers [J]. Artificial Intelligence, 2001, 125: 209～226
[126] P H Winston. Artificial intelligence [M]. MA: Addison- Wesley, 1992
[127]王有元,廖瑞金,孙才新,等.变压器油中溶解气体浓度灰色预测模型的改进[J].高电压技术,2003,29(4):24～26
[128]袁保奎,郭基伟,唐国庆.应用灰色理论预测变压器等充油设备内的油中气体浓度[J].电力系统及其自动化学报,2001,13(3):40～42
[129]罗运拍,于萍,宋斌,等.用灰色模型预测变压器油中溶解气体的含量[J].中国电机工程学报,2001,21(3):65～69
[130]王晶,刘建新.基于灰色新预测模式的变压器故障预测[J].华北电力大学学报,2007,34(1):10～14
[131] Bates J M,Granger C W J.Combination of forecasts[J].Journal of Operational Research Quarterly, 1969,(20):451～468
[132] Chen K Y,Wang C H.Support vector regression with genetic algorithms in forecasting tourism demand[J].Tourism Manage,2007,2 (1):215～226
[133] Lai J.B,Wang H.Q,Zhu L..Study of network security situation awareness model based on simple additive weight and grey theory[C]. International Conference on Computational Intelligence and Security,Guangzhou, China:Guangdong University of Technology,2006,1545～1548
[134] Pai P F.System reliability forecasting by support vector machines with genetic algorithms [J]. Mathematics Computer Model,2006,4(3):262～274
[135]傅立.灰色系统理论及其应用[M].北京:科学技术文献出版社,1991,83～85
[136] Liu Sifeng, Lin Yi. An Introduction to Grey System: Foundations Methodology's and Applications [M]. Slippery Rock: IIGSS Academic Publisher,1998
[137]刘琼,刘琳,刘全.用最优化灰色理论进行电力负荷预测的研究.东北电力技术,1998,30(3):17～21
[138]杨大地,涂光裕编.数值分析[M].重庆:重庆大学出版社,1998
[139]唐小我.组合预测误差信息矩阵研究[J].电子科技大学学报,1992,21(4):448～454
[140]马永开,唐小我,杨桂元.非负权重最优组合预测方法的基本理论研究[J].运筹与管理,1997,6(2):1～8
[141]陈华友,侯定丕.基于预测有效度的优性组合预测模型的研究[J].中国科学技术大学学报,2002,32(2):172～180
[142]王应明.基于相关性的组合预测方法研究[J].预测,2002,21(2):58～62
[143]林茂六,陈春雨.基于傅立叶核与径向基核的支持向量机性能之比较[J].重庆邮电学院学报,2005,17(6):647～650