智能信息处理理论的电力变压器故障诊断方法
|
摘要
研究电力变压器故障诊断方法对提高电力系统运行的安全性和可靠性具有决定性意义,同时也具有重要的理论价值和广阔的工程应用前景。电力变压器故障诊断方法包括电力变压器的状态评估、故障诊断和故障预测。针对电力变压器故障诊断数据小样本、贫信息的特点,研究以智能信息处理理论为技术主线的电力变压器故障诊断方法,主要研究内容如下:
⑴智能信息处理方法的关键技术研究。在研究分析智能信息处理理论关键技术的基础上,提出了以智能信息处理理论为主线的电力变压器故障诊断技术方案。
⑵电力变压器状态评估算法研究。确定了电力变压器状态评估指标体系,研究基于云模型白化权函数的灰聚类分析和改进的加权灰靶理论相结合的电力变压器状态评估模型,通过先验知识和实验分析共同优化云模型参数,研究以正常电力变压器数据为靶心的改进的加权灰靶理论算法,优化状态分级。本文提出的电力变压器状态评估算法能够正确评估电力变压器的状态,提高了电力变压器状态评估的科学性和客观性。
⑶电力变压器故障诊断算法研究。模拟生物免疫系统的特点,设计两级分类器级联的电力变压器故障诊断算法,研究遗传支持向量机判断电力变压器故障和正常状态的初级分类器,研究灰关联度度量抗体与抗原之间亲和力的灰色人工免疫算法和动态疫苗机制的高频变异算子。本文提出的电力变压器故障诊断算法对电力变压器单一故障和多故障都能够有效的分类,提高了电力变压器故障诊断的准确率。
⑷油中溶解气体浓度预测算法研究。针对电力变压器油中溶解气体浓度随时间变化的特征,研究灰色Verhulst模型和双层GM(1,1,ρ)新陈代谢模型并联的预测方法,该预测模型能够全面反映气体浓度的变化特征,具有较高的预测精度。
⑸电力变压器故障诊断软件平台开发和数据库构建。开发电力变压器故障诊断和预测软件平台,实现电力变压器的状态评估、故障诊断和预测等主要功能。构建电力变压器状态评估、故障诊断和油中溶解气体浓度预测数据库,设计并实现了数据库的相关功能。
本文对电力变压器故障诊断方法进行了系统的研究,对电力变压器故障诊断技术的发展具有重要的理论意义和实用价值。
As China's power grid develops to the direction of highly automation, people’s livelihood demands on security and reliability of long lasting electricity power supply increase. Condition based maintenance system based on on-line monitoring and fault diagnosis urgently need to be established. A power transformer is one of the key equipments in the power system, therefore its operating condition directly affects the power system running state. Research of power transformer fault diagnosis methods has important theoretical value and broad engineering application prospects, and it is important for the power system to raise operational safety and reliability. Research of power transformer fault diagnosis methods is also meaningful for the power transformers to reduce maintenance costs and economic loss caused by their faults. Power sectors in domestic and abroad attach importance to research of power transformer fault diagnosis methods.
Power transformer fault diagnosis methods include power transformer condition assessment, fault diagnosis and fault forecast three objectives. Power transformer condition assessment uses monitor characteristic parameters of power transformer to evaluate its running status, and then formulates the appropriate maintenance strategy. Power transformer fault diagnosis is to determine its fault type and fault location. Power transformer fault prediction forecasts power transformer running trends based on available data. Dissolved gases analysis method analyzes composition, content and gas production rate of hydrogen, methane, ethane, acetylene, ethylene and other gases dissolved in transformer oil to determine power transformer latent fault types and its location. DGA is an international general technical means for power transformer fault diagnosis. Intelligent information processing theory provides new ideas to power transformer fault diagnosis method for its development and progress. Power transformer fault diagnosis data are small samples, and have less information. It is an important problem to be solved in power transformer fault diagnosis technology that how to deal with and utilize such data.
This thesis began with present research of the power transformers condition assessment, fault diagnosis and fault prediction, therefore proposed a power transformer fault diagnosis technic system consist of the power transformers condition assessment, fault diagnosis and dissolved gases concentrations prediction. In the thesis, the research of the power transformers condition assessment, fault diagnosis and dissolved gases concentrations prediction algorithms based on intelligent information processing theory was designed for characteristics of power transformer data. The thesis also developed a power transformer fault diagnosis software platform and built a power transformer fault diagnosis database. The main research contents included the following:
⑴Research of key technologies in intelligent information processing theory
In this thesis, cloud model, gray system theory, artificial immune system, genetic algorithm, support vector machine and other key technology features were studied and analyzed to solved problems of power transformer condition assessment, fault diagnosis and dissolved gas content prediction. These theories were experimented on power transformer fault diagnosis to analysis their features. The power transformers fault diagnosis technical program based on intelligent information processing theory was proposed. Research of key technologies in intelligent information processing theory provided theoretical basis for the power transformers fault diagnosis.
⑵Research of power transformer condition assessment algorithm
Power transformer condition assessment algorithm proposed in the thesis optimized evaluation index system of power transformer condition, which includes dissolved gases analysis, electrical test, insulating oil characteristics analysis and qualitative indicators. Gray cluster analysis based on cloud model white function combined with improved weighted gray target theory evaluated transformer condition was researched under the power transformer condition evaluation index system. Cloud model is good at analyzing fuzzy, random uncertainty problems. Gray cluster analysis has advantages of solving small samples and poor information gray system. Gray cluster analysis based on cloud model white function is suitable for fuzziness, randomness and gray of power transformer condition assessment. Related standards and priori knowledge about power transformer index both optimized cloud model parameters. For power transformer condition assessments only based on dissolved gases analysis data, an improved weighted gray target theory whose center of the gray target is the normal power transformer DGA data was proposed, and its boundaries of condition classification was optimized by power transformers fault severity distribution priori knowledge. Qualitative indicators were determined by expert scoring system. Experiments show that power transformer condition assessment model combine gray cluster analysis based on cloud model white function with improved weighted gray target theory is able to correctly evaluate power transform condition, raises the scientific and objective of power transformer condition assessment, and has practical significance on power transformer condition assessment and other assessment areas.
⑶Research of power transformer fault diagnosis algorithm
Biological immune system firstly judges whether a cell is autologous cell or not. Then, non-autologous cell is deal with high affinity antibody. The power transformers fault diagnosis algorithm addressed in the thesis was composed of 2 cascade classifiers, which simulated biological immune system process. Firstly, primary classifier determined whether a power transformer is normal. Support vector machine classified power transformer status into normal or fault two categories because power transformer fault diagnosis data was small samples and had poor information. Both support vector machine kernel function and function parameters affect the results of support vector machine classification. Genetic algorithm is global search optimization method. Support vector machine kernel function selected radial basis function. Genetic algorithm optimized parameters of radial basis function. Secondly, best memory antibody set of artificial immune algorithm were trained. Gray relational analysis measured the affinity between antibody and antigen, which was called gray artificial immune algorithm. In gray artificial immune algorithm, priori knowledge of power transformer fault diagnosis constructed vaccine set. High-frequency mutation operator based on the dynamic vaccine was constructed. The best memory antibody set was trained depending on the fault type. Finally, 5 neighbors integrated decision making method diagnosed detail type of power transformer fault according to the best memory antibody set. The 5 neighbors integrated decision making method integrated diagnosed fault type according to both vote number and rank location. Experiments indicate that the power transformer fault diagnosis algorithm combined genetic SVM with gray artificial immune algorithm based on dynamic vaccine can diagnosis power transformer single and multiple faults effectively and improves diagnosis accuracy.
⑷Research of dissolved gases concentrations prediction algorithm
Power transformer dissolved gases concentrations prediction is the basis for fault prediction because it provides data for power transformer fault prediction. The thesis analyzed the characteristics of power transformer oil dissolved gases concentration data varying with time, and then designed a prediction model composed of two parallel models. For DGA data with single peak, gray Verhulst model forecasted power transformer dissolved gases concentrations. Gray Verhulst model and its improved forms were compared by experiments. For DGA data with monotonically increasing feature, Double-Layer GM(1,1,ρ) Metabolism model was used to predict power transformer dissolved gases concentrations. In Double-Layer GM(1,1,ρ) Metabolism model, GM (1,1,ρ) model forecasted both original data and residuals. Markov chain controlled the polarity between the two predictions. The metabolic parameter was analyzed. Experiments demonstrate that the dissolved gas concentrations prediction algorithm based on gray Verhulst model and DLGMM(1,1,ρ) model can fully reflect the dissolved gases concentration variation, therefore it has higher prediction accuracy.
⑸Power transformer fault diagnosis software platform development and power transformer fault diagnosis database construction
MATLAB GUI and M language developed power transformer fault diagnosis and prediction software platform, and achieved main functions such as power transformer condition assessment, improved three-ration method and the Gaussian gray cluster analysis fault diagnosis, dissolved gases concentration and fault prediction, save new data and printout based on former power transformer condition assessment, fault diagnosis and dissolved gases concentrations prediction algorithms. Access database software built power transformer fault diagnosis database according to power transformer condition assessment, fault diagnosis and dissolved gas concentrations prediction data features. The power transformer fault diagnosis database included power transformer condition assessment sub-database, fault diagnosis sub-database and dissolved gases concentrations prediction sub-database. Corresponding attributes, tuples and primary keys of each relation sheet in sub-database were designed. Window, query and report operations were realized.
This thesis systematically researched power transformer condition assessment, fault diagnosis and dissolved gases concentrations prediction, and detailed studied and analyzed key technologies of intelligent information processing theory. The thesis developed power transformer fault diagnosis and prediction software platform, and constructed power transformer fault diagnosis database. Research work in the thesis has important theoretical significance and practical value on development of power transformer fault diagnosis method. The intelligent information processing algorithms proposed in the thesis also have some reference value and broad engineering application prospects to solve related problems.
⑴智能信息处理方法的关键技术研究。在研究分析智能信息处理理论关键技术的基础上,提出了以智能信息处理理论为主线的电力变压器故障诊断技术方案。
⑵电力变压器状态评估算法研究。确定了电力变压器状态评估指标体系,研究基于云模型白化权函数的灰聚类分析和改进的加权灰靶理论相结合的电力变压器状态评估模型,通过先验知识和实验分析共同优化云模型参数,研究以正常电力变压器数据为靶心的改进的加权灰靶理论算法,优化状态分级。本文提出的电力变压器状态评估算法能够正确评估电力变压器的状态,提高了电力变压器状态评估的科学性和客观性。
⑶电力变压器故障诊断算法研究。模拟生物免疫系统的特点,设计两级分类器级联的电力变压器故障诊断算法,研究遗传支持向量机判断电力变压器故障和正常状态的初级分类器,研究灰关联度度量抗体与抗原之间亲和力的灰色人工免疫算法和动态疫苗机制的高频变异算子。本文提出的电力变压器故障诊断算法对电力变压器单一故障和多故障都能够有效的分类,提高了电力变压器故障诊断的准确率。
⑷油中溶解气体浓度预测算法研究。针对电力变压器油中溶解气体浓度随时间变化的特征,研究灰色Verhulst模型和双层GM(1,1,ρ)新陈代谢模型并联的预测方法,该预测模型能够全面反映气体浓度的变化特征,具有较高的预测精度。
⑸电力变压器故障诊断软件平台开发和数据库构建。开发电力变压器故障诊断和预测软件平台,实现电力变压器的状态评估、故障诊断和预测等主要功能。构建电力变压器状态评估、故障诊断和油中溶解气体浓度预测数据库,设计并实现了数据库的相关功能。
本文对电力变压器故障诊断方法进行了系统的研究,对电力变压器故障诊断技术的发展具有重要的理论意义和实用价值。
As China's power grid develops to the direction of highly automation, people’s livelihood demands on security and reliability of long lasting electricity power supply increase. Condition based maintenance system based on on-line monitoring and fault diagnosis urgently need to be established. A power transformer is one of the key equipments in the power system, therefore its operating condition directly affects the power system running state. Research of power transformer fault diagnosis methods has important theoretical value and broad engineering application prospects, and it is important for the power system to raise operational safety and reliability. Research of power transformer fault diagnosis methods is also meaningful for the power transformers to reduce maintenance costs and economic loss caused by their faults. Power sectors in domestic and abroad attach importance to research of power transformer fault diagnosis methods.
Power transformer fault diagnosis methods include power transformer condition assessment, fault diagnosis and fault forecast three objectives. Power transformer condition assessment uses monitor characteristic parameters of power transformer to evaluate its running status, and then formulates the appropriate maintenance strategy. Power transformer fault diagnosis is to determine its fault type and fault location. Power transformer fault prediction forecasts power transformer running trends based on available data. Dissolved gases analysis method analyzes composition, content and gas production rate of hydrogen, methane, ethane, acetylene, ethylene and other gases dissolved in transformer oil to determine power transformer latent fault types and its location. DGA is an international general technical means for power transformer fault diagnosis. Intelligent information processing theory provides new ideas to power transformer fault diagnosis method for its development and progress. Power transformer fault diagnosis data are small samples, and have less information. It is an important problem to be solved in power transformer fault diagnosis technology that how to deal with and utilize such data.
This thesis began with present research of the power transformers condition assessment, fault diagnosis and fault prediction, therefore proposed a power transformer fault diagnosis technic system consist of the power transformers condition assessment, fault diagnosis and dissolved gases concentrations prediction. In the thesis, the research of the power transformers condition assessment, fault diagnosis and dissolved gases concentrations prediction algorithms based on intelligent information processing theory was designed for characteristics of power transformer data. The thesis also developed a power transformer fault diagnosis software platform and built a power transformer fault diagnosis database. The main research contents included the following:
⑴Research of key technologies in intelligent information processing theory
In this thesis, cloud model, gray system theory, artificial immune system, genetic algorithm, support vector machine and other key technology features were studied and analyzed to solved problems of power transformer condition assessment, fault diagnosis and dissolved gas content prediction. These theories were experimented on power transformer fault diagnosis to analysis their features. The power transformers fault diagnosis technical program based on intelligent information processing theory was proposed. Research of key technologies in intelligent information processing theory provided theoretical basis for the power transformers fault diagnosis.
⑵Research of power transformer condition assessment algorithm
Power transformer condition assessment algorithm proposed in the thesis optimized evaluation index system of power transformer condition, which includes dissolved gases analysis, electrical test, insulating oil characteristics analysis and qualitative indicators. Gray cluster analysis based on cloud model white function combined with improved weighted gray target theory evaluated transformer condition was researched under the power transformer condition evaluation index system. Cloud model is good at analyzing fuzzy, random uncertainty problems. Gray cluster analysis has advantages of solving small samples and poor information gray system. Gray cluster analysis based on cloud model white function is suitable for fuzziness, randomness and gray of power transformer condition assessment. Related standards and priori knowledge about power transformer index both optimized cloud model parameters. For power transformer condition assessments only based on dissolved gases analysis data, an improved weighted gray target theory whose center of the gray target is the normal power transformer DGA data was proposed, and its boundaries of condition classification was optimized by power transformers fault severity distribution priori knowledge. Qualitative indicators were determined by expert scoring system. Experiments show that power transformer condition assessment model combine gray cluster analysis based on cloud model white function with improved weighted gray target theory is able to correctly evaluate power transform condition, raises the scientific and objective of power transformer condition assessment, and has practical significance on power transformer condition assessment and other assessment areas.
⑶Research of power transformer fault diagnosis algorithm
Biological immune system firstly judges whether a cell is autologous cell or not. Then, non-autologous cell is deal with high affinity antibody. The power transformers fault diagnosis algorithm addressed in the thesis was composed of 2 cascade classifiers, which simulated biological immune system process. Firstly, primary classifier determined whether a power transformer is normal. Support vector machine classified power transformer status into normal or fault two categories because power transformer fault diagnosis data was small samples and had poor information. Both support vector machine kernel function and function parameters affect the results of support vector machine classification. Genetic algorithm is global search optimization method. Support vector machine kernel function selected radial basis function. Genetic algorithm optimized parameters of radial basis function. Secondly, best memory antibody set of artificial immune algorithm were trained. Gray relational analysis measured the affinity between antibody and antigen, which was called gray artificial immune algorithm. In gray artificial immune algorithm, priori knowledge of power transformer fault diagnosis constructed vaccine set. High-frequency mutation operator based on the dynamic vaccine was constructed. The best memory antibody set was trained depending on the fault type. Finally, 5 neighbors integrated decision making method diagnosed detail type of power transformer fault according to the best memory antibody set. The 5 neighbors integrated decision making method integrated diagnosed fault type according to both vote number and rank location. Experiments indicate that the power transformer fault diagnosis algorithm combined genetic SVM with gray artificial immune algorithm based on dynamic vaccine can diagnosis power transformer single and multiple faults effectively and improves diagnosis accuracy.
⑷Research of dissolved gases concentrations prediction algorithm
Power transformer dissolved gases concentrations prediction is the basis for fault prediction because it provides data for power transformer fault prediction. The thesis analyzed the characteristics of power transformer oil dissolved gases concentration data varying with time, and then designed a prediction model composed of two parallel models. For DGA data with single peak, gray Verhulst model forecasted power transformer dissolved gases concentrations. Gray Verhulst model and its improved forms were compared by experiments. For DGA data with monotonically increasing feature, Double-Layer GM(1,1,ρ) Metabolism model was used to predict power transformer dissolved gases concentrations. In Double-Layer GM(1,1,ρ) Metabolism model, GM (1,1,ρ) model forecasted both original data and residuals. Markov chain controlled the polarity between the two predictions. The metabolic parameter was analyzed. Experiments demonstrate that the dissolved gas concentrations prediction algorithm based on gray Verhulst model and DLGMM(1,1,ρ) model can fully reflect the dissolved gases concentration variation, therefore it has higher prediction accuracy.
⑸Power transformer fault diagnosis software platform development and power transformer fault diagnosis database construction
MATLAB GUI and M language developed power transformer fault diagnosis and prediction software platform, and achieved main functions such as power transformer condition assessment, improved three-ration method and the Gaussian gray cluster analysis fault diagnosis, dissolved gases concentration and fault prediction, save new data and printout based on former power transformer condition assessment, fault diagnosis and dissolved gases concentrations prediction algorithms. Access database software built power transformer fault diagnosis database according to power transformer condition assessment, fault diagnosis and dissolved gas concentrations prediction data features. The power transformer fault diagnosis database included power transformer condition assessment sub-database, fault diagnosis sub-database and dissolved gases concentrations prediction sub-database. Corresponding attributes, tuples and primary keys of each relation sheet in sub-database were designed. Window, query and report operations were realized.
This thesis systematically researched power transformer condition assessment, fault diagnosis and dissolved gases concentrations prediction, and detailed studied and analyzed key technologies of intelligent information processing theory. The thesis developed power transformer fault diagnosis and prediction software platform, and constructed power transformer fault diagnosis database. Research work in the thesis has important theoretical significance and practical value on development of power transformer fault diagnosis method. The intelligent information processing algorithms proposed in the thesis also have some reference value and broad engineering application prospects to solve related problems.
引文
[1]中电联发布2008年电力工业统计年报数据[EB/OL].[2009-9-1]. http://www.cec.org.cn/html/news/2009/7/7/200977931151116.html.
[2] 2008年全国电力市场分析预测(秋季)报告[EB/OL].[2009-9-1]. http://www.chinasperi.com.cn/upload/attachment/200931152222177.pdf.
[3]中国电网发展情况[EB/OL].[2009-9-2].http://baike.baidu.com/view/69135.htm.
[4]我国电网建设实现大跨步前进[EB/OL].[2009-9-2]. http://info.electric.hc360.com/2007/10/10111460791.shtml.
[5]孙才新,陈伟根,李俭,等.电气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.
[6]中国供电可靠率达99.91%专家吁制订准入规则[EB/OL].[2009-9-2]. http://www.dtp-lc.com/m/tyxw/2008116232320.htm.
[7]南方电网公司发布2008年社会责任报告[EB/OL].[2009-9-2]. http://www.cnr.cn/newscenter/gnxw/200906/t20090605_505356963_1.html.
[8] Mizutani Y, Takahashi T, Ito T. Lifetime evaluation method for pole transformer based on transient temperature analysis[J]. IEEE Transactions on Power and Energy, 2007, 127(5):653-658.
[9] Zhao W Q , Zhu Y L. A prediction model for dissolved gas in transformer oil based on improved verhulst grey theory[C]. Proceedings of 3rd IEEE Conference on Industrial Electronics and Applications, 2008, June 3-5:2042-2044. Piscataway: IEEE. Computer Society, 2008.
[10] Sinqh A ,Verma P. A review of intelligent diagnostic methods for condition assessment of insulation system in power transformers[C]. Proceedings of 2008 International Conference on Condition Monitoring and Diagnosis, 2008, April 21-24:1354-1357. Piscataway: IEEE. Computer Society, 2007.
[11] [EB/OL].[2009-9-6]. http://159.226.244.28/portal/proj_search.asp.
[12] Morais R M , Mannheimer W A , Carballeira M , et al. Furfural analysis for assessing degradation of thermally upgraded papers in transformer insulation[J]. IEEE Transactions on Dielectrics and Electrical Insulation,1999,6(2):159-163.
[13]操敦奎.变压器油中气体分析诊断与故障检查[M].北京:中国电力出版社,2005.
[14] IEC. Interpretation of the analysis of gases in transformers and other oil-filled electrical equipment in service[S]. IEC Publication 599 (First Edition), 1978.
[15]中华人民共和国电力工业部.DL/T596-1996电力设备预防性试验规程[S].北京:中国电力出版社,1997.
[16]中华人民共和国国家经济贸易委员会.DL/T722-2000变压器油中溶解气体分析和判断导则[S].北京:中国电力出版社,2001.
[17]章政.基于遗传编程的电力变压器绝缘故障诊断模型研究[D].上海:上海交通大学,2007.
[18]李建坡.基于油中溶解气体分析的电力变压器故障诊断技术的研究[D].长春:吉林大学,2008.
[19]黄华,傅晨钊.大型电力变压器状态分析综述[J].华东电力,2004,32(3):23-26.
[20] Suwanasri T, Chaidee E, Adsoongnoen C. Failure statistics and power transformer condition evaluation by dissolved gas analysis technique[C]. Proceedings of 2008 International Conference on Condition Monitoring and Diagnosis,2008, April 21-24:492-496. Piscataway: IEEE. Computer Society, 2007.
[21]张小奇.变压器故障预报与状态评估的研究[D].保定:华北电力大学,2006.
[22]张成林,王克英.变电站设备状态检修现状分析及前景展望[C].中国高等学校电力系统及其自动化专业第二十二届学术会论文摘要集,2006.
[23]纪航.变压器状态综合评估方法研究[D].保定:华北电力大学,2005.
[24]廖玉祥.一种电力变压器运行状态综合评估模型的研究[D].重庆:重庆大学,2006.
[25] Fuhr J., Aschwanden T. Experience with Diagnostic Tools for Condition Assessment of Large Power Transformers[C]. IEEE International Symposium on Electrical Insulation, 2004, Septemper 19-22:508-511. Piscataway: IEEE,2004.
[26] Yadav R ,Kumar S ,Venkatasami A , et al. Condition based maintenance of power transformer: A case study[C]. Proceedings of 2008 International Conference on Condition Monitoring and Diagnosis, 2008, April 21-24:502-504. Piscataway: IEEE. Computer Society, 2007.
[27] Rusov V ,Zhivodernikov S. Transformer condition monitoring[C] .Proceedings of 2008 International Conference on Condition Monitoring and Diagnosis, 2008, April 21-24:1012-1014. Piscataway:IEEE. Computer Society, 2007.
[28] Muhr M , Sumereder C. Transformer condition determination methods[C]. Proceedings of the International Symposium on Electrical Insulating Materials,2008,Septermber 7-11:479-482. Tokyo: Institute of Electrical Engineers of Japan,2008.
[29]中国电力网.浙江省电力公司状态检修验收工作全面完成[WB/OL].(2008-10-06)[2009-9-7]. http://www.chinapower.com.cn/newsarticle/1075/new1075926.asp.
[30]国家电网.重庆公司状态检修工作通过国家电网公司验收[WB/OL].(2009-08-25)[2009-9-7]. http://www.sgcc.com.cn/xwzx/gsxw/2009/08/205006.shtml.
[31]陕西省电力公司.陕西省电力公司顺利通过国家电网公司状态检修工作验收[WB/OL].(2009-7-1)[2009-9-7].http://www.sxcoal.com/dlm/2009/07/01/470020/article.html.
[32]熊浩,孙才新,杜鹏,等.基于物元理论的电力变压器状态综合评估[J].重庆大学学报(自然科学版),2006,29(10):24-28.
[33]赵文清,朱永利.电力变压器状态评估综述[J].变压器,2007,44(11):9-12,74.
[34]吴莉琳,蔡玉广.分层式信息融合在变压器状态评估中的应用[J].广东电力,2007,20(2):29-32.
[35]廖瑞金,王谦,骆思佳,等.基于模糊综合评判的电力变压器运行状态评估模型[J].电力系统自动化,2008,32(3):70-75.
[36] Wang Y Y, Zhou J J,Chen W G., et al. Assessment method for the reliability of power transformer based on fault-tree analysis[J]. High Voltage Engineering,2009,35(3):514-520.
[37] Tang W H, Spurgeon K ,Wu Q H,et al. An evidential reasoning approach to transformer condition assessment [J]. IEEE TRANSACTIONS ON POWER DELIVERY,2004,19(4):1696-1703.
[38] Utami N Y, Tamsir Y, Pharmatrisanti A, et al. Evaluation condition of transformer based on infrared thermography results[C]. Proceedings of the IEEE International Conference on Properties and Applications of Dielectric Materials,2009, July 19-23:1055-1058. Piscataway: Institute of Electrical and Electronics Engineers Inc.,2009.
[39] Xie Q, Li Y Q, Xie H.L., et al. Large power transformer condition evaluation based on multilevel extension theory[C]. Proceedings of 3rd International Conference on Deregulation and Restructuring and Power Technologies,2008, April 6-9:933-936. Piscataway:IEEE. Computer Society, 2008.
[40] Wei J L, Zhang G. J, Dong M ,et al. Research progress on insulation condition estimation for oil-immersed power transformer[C]. Proceedings of the International Symposium on Electrical Insulating Materials,2008, September 7-11:32-35.Tokyo: Institute of Electrical Engineers ofJapan,2008.
[41] Németh B, Laboncz S, Kiss I. Condition monitoring of power transformers using DGA and fuzzy logic[C]. Proceedings of 2009 IEEE Electrical Insulation Conference,2009, May 31-June 3:373-376. Piscataway:IEEE. Computer Society, 2009.
[42]熊浩,孙才新,张昀,等.电力变压器运行状态的灰色层次评估模型[J].电力系统自动化,2007,31 (7):55-60.
[43]徐敏,钟文慧,刘井萍.基于模糊理论的变压器状态评估[J].南昌大学学报(工学版),2009,31(1): 53-56.
[44]袁志坚,孙才新,袁张渝,等.变压器健康状态评估的灰色聚类决策方法[J].重庆大学学报(自然科学版),2005,28(3):22-25.
[45]罗逸,邓聚龙,郑家粲,等.腐蚀评估中的灰靶方法[J].中国腐蚀与防护学报,2001,21(6):374-378.
[46]陈士玮,李柱国,徐启圣.基于灰靶理论和油液监测的磨损模式识别研究[J].中国矿业大学学报, 2005,34(3):353-357.
[47]范体军.基于灰靶理论的设备故障模式识别[J].中国设备工程,2004,(3):10-13.
[48]朱永利,申涛,李强.基于支持向量机和DGA的变压器状态评估方法[J].电力系统及其自动化学报,2008,20(6):111-115.
[49]申涛,朱永利,李强,等.基于支持向量机和DGA的变压器状态评估方法[J].电力科学与程,2008, 24(2):47-50.
[50] Hu H S, Qian S X, Wang J, et al. Application of information fusion technology in the remote state on-line monitoring and fault diagnosing system for power transformer[C]. Proceedings of 8th International Conference on Electronic Measurement and Instruments, 2007, August 16-18: 3550-3555. Piscataway:IEEE. Computer Society, 2007.
[51]谢红玲,律方成.基于信息融合的变压器状态评估方法研究[J].华北电力大学学报,2006,33(3): 8-11.
[52]郑蕊蕊.基于灰色系统理论的电力变压器故障诊断技术[D].长春:吉林大学大学,2007.
[53]章政,杨荆林,肖登明等.基于油中溶解气体分析的变压器绝缘故障诊断方法的研究和发展[J].电力设备,2004,5(1):20-24.
[54] Kalavathi M S, Reddy B R, Singh B P. Transformer fault diagnosis using fuzzy logic and neural network[C]. Annual Report-Conference on Electrical Insulation and Dielectric Phenomena,2005, October 16-19:486-489. Piscataway:IEEE. Computer Society, 2005.
[55] Wu T, Tu G.Y, Bo Z Q, et al. Fuzzy set theory and fault tree analysis based method suitable for fault diagnosis of power transformer[J]. International Conference on Intelligent Systems Applications to Power Systems,2007,1-5.
[56] Zheng X X. Intelligent fault diagnosis of power transformer based on fuzzy logic and rough set theory[C]. Proceedings of the World Congress on Intelligent Control and Automation,2008, June 25-27:6852-6857. Piscataway:IEEE. Computer Society, 2008.
[57] Purkait P,Chakravorti S. Time and frequency domain analysis based expert system for impulse fault diagnosis in transformers[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2002,9(3): 433-445.
[58]苏文辉,鞠平.模糊专家系统在电力变压器在线故障诊断中的应用初探[J].电力系统及其自动化,2002,14(1):12-14.
[59] Guo Y.J.,Sun L. H.,Liang Y.C.,et al. The fault diagnosis of power transformer based on improved RBF neural network[C]. Proceedings of the Sixth International Conference on Machine Learningand Cybernetics, 2007, August 19-22,v2:1111-1114. Piscataway:IEEE. Computer Society, 2007.
[60] Georgilakis P S, Katsigiannis J A, Valavanis K P, et al. A systematic stochastic Petri net based methodology for transformer fault diagnosis and repair actions[J]. Journal of Intelligent and Robotic Systems: Theory and Applications, 2006, 45(2):181-201.
[61] Li C. Qian H., Yang X H, et al. The fault diagnosis of power transformer using clustering and radial basis function neural network[C]. Proceedings of the 2009 International Conference on Machine Learning and Cybernetics,2009, July 12-15,v3: 1257-1260. Piscataway:IEEE. Computer Society, 2009.
[62]王伟.油浸式电力变压器故障诊断技术的研究[D].济南:山东大学,2008.
[63] Li P , LüF C, Li N Y, et al. Study on fault diagnosis for power transformer based on cloud matter element analysis principle and DGA[C]. Proceedings of the IEEE International Conference on Properties and Applications of Dielectric Materials, 2009, July 19-23:244-248. Piscataway:IEEE., 2009.
[64] Shu H C, Hu Z J, Sun S Y, et al. The fault diagnosis algorithm for transformer based on Extenics and rough set theory [C]. Proceeding of 3rd International Conference on Deregulation and Restructuring and Power Technologies, 2008, April6-April9:1269-1272. Piscataway:IEEE. Computer Society, 2008.
[65] Wu Z L, Zhang B, ZhuY L, et al. Transformer fault portfolio diagnosis based on the combination of the multiple Bayesian classifier and SVM[C]. Proceedings of 2009 International Conference on Electronic Computer Technology, 2009, February 20-22:379-382. Piscataway:IEEE. Computer Society, 2009.
[66] Liu X,Dong D C,Wan G. C. Global fault diagnosis method of traction transformer based on improved fuzzy cellular neural network[C]. 4th IEEE Conference on Industrial Electronics and Applications,2009, May25-27:353-357. Piscataway:IEEE. Computer Society, 2009.
[67] Song B P, Chen M B. Power transformer DGA integrated diagnosis system based on Oracle database[C]. 2009 Asia-Pacific Power and Energy Engineering Conference, 2009, March 27-31. Piscataway:IEEE. Computer Society, 2009.
[68]陆静.蚁群算法在变压器故障诊断中的应用研究[D].南京:南京航空航天大学,2008.
[69]杨淑莹.模式识别与智能计算:Matlab实现[M].北京:电子工业出版社,2008.1:133-139,298-318.
[70] Li J S, Zhao X F, Sun Z Q, et al. The application of chaos support vector machines in transformer fault diagnosis[C]. Proceedings of the IEEE International Conference on Properties and Applications of Dielectric Materials,2009, July19-23:236-239. Piscataway:IEEE., 2009.
[71] Zhu Y L, Zheng J B, Wang F. Transformer fault diagnosis based on Euclidean clustering and support vector machines [C]. Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition,2007, November 2-4,v4:1453-1456. Piscataway:IEEE., 2007.
[72]蒋延军,倪远平.基于粗糙集与支持向量机的变压器故障诊断法[J].高电压技术,2008,34(80): 1755-1760.
[73] Zang H Z, Yu X D. Transform fault diagnosis utilizing rough set and support vector machine[C]. 2009 Asia-Pacific Power and Energy Engineering Conference,2009, March 27-31. Piscataway:IEEE. Computer Society, 2009.
[74]焦李成,杜海峰,刘芳,等著.免疫优化计算、学习与识别[M].北京:科学出版社,2006:1-39,45-46.
[75]邓晓刚,田学民.基于免疫核主元分析的故障诊断方法[J].清华大学学报(自然科学版),2008,48 (S2):1794-1798.
[76]刘欣,黄席樾,李伟,等.基于免疫原理的多Agent故障诊断系统[J].计算机仿真,2006,23(9): 153-156,201.
[77]熊浩,孙才新,陈伟根,等.电力变压器故障诊断的人工免疫网络分类算法[J].电力系统自动化, 2006,30(6):57-60.
[78]周爱华,张彼德,张厚宣.基于人工免疫分类算法的电力变压器故障诊断[J].高电压技术,2007,33 (8):77,87,97,08.
[79]李红芳,张清华,谢克明.一种新型免疫网络学习算法在故障诊断中的应用[J].智能系统学报,2008,3(5):449-454.
[80]彭媛,张春良,赵辉,等.基于人工免疫系统的核动力设备故障诊断[J].核动力工程,2008,29(2): 124-128.
[81]宋绍民,王耀南,孙炜,等.基于免疫聚类算法的变压器DGA数据故障诊断方法[J].变压器,2008, 45(8):70-72
[82]刘思峰.灰色系统理论的产生、发展及前沿动态[J].浙江万里学院学报,2003,16(4):14-17.
[83]邓聚龙著.灰理论基础[M].武汉:华中科技大学出版社,2002.2:1-307.
[84] Dong M, Yan Z,Yasuhiko T. Fault diagnosis of power transformer based on model-diagnosis with grey relation[C]. Proceedings of the International conference on properties and applications of dielectric materials,2003, June1-5:1158-1161. Piscataway:IEEE., 2003.
[85]李建坡,赵继印,郑蕊蕊.基于综合关联度分析的电力变压器故障诊断[J].吉林大学学报(信息科学版)2008,26(1):62-68.
[86]李俭,孙才新,陈伟根等.基于灰色聚类分析的充油电力变压器绝缘故障诊断的研究[J].电工技术学报,2002,17(4):24-29.
[87]柳纲,郭基伟,唐国庆.基于灰色位势理论的变压器故障诊断算法[J].电力系统及其自动化学报, 2004,16(2):54-57.
[88]曹建,范竞敏,丁家峰,等.改进的灰熵关联度算法用于变压器故障诊断[J].电力系统及其自动化学报,2009,21(4):25-29,87.
[89]伍伟慧.基于改进型灰色关联的变压器故障诊断[D].长沙:长沙理工大学,2009.
[90] Bochenski B, Mosinski F, Piotrowski T. Monitoring and forecasting of power transformer ratings using DTR computer program[C]. EUROCON 2007 - The International Conference on Computer as a Tool, 2007, September9-12:1373-1377. Piscataway:IEEE. Computer Society, 2007.
[91] Wang J, Liu J X, Wu L Z. Fault forecast of the transformer using new theory of grey system[C]. IET Conference Publications,2006, September17-20:340-345. Stevenage: Institution of Engineering and Technology,2006.
[92]孙才新,毕为民,周泉,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.
[93]肖燕彩,朱衡君,陈秀海.用灰色多变量模型预测变压器油中溶解的气体浓度[J].电力系统自动化,2006,30(13):64-67.
[94]费胜巍,孙宇.融合粗糙集与灰色理论的电力变压器故障预测[J].中国电机工程学报,2008,28 (16):154-160.
[95]杨廷方,刘沛,李浙,等.应用新型多方法组合预测模型估计变压器油中溶解气体浓度[J].中国电机工程学报,2008,28(31):108-113.
[96]乔俊玲.人工神经网络在变压器油中溶解气体分析中的应用研究[D].北京:北京交通大学,2007.
[97] Li W J,Zhang Y,Guo W Q, et al. Forecasting dissolved gases content in power transformer oil based on particle swarm optimization-based RBF neural network[C]. 2009 Second ISECS International Colloquium on Computing, Communication, Control, and Management,2009,August8-9,v3:153-155. Piscataway:IEEE. Computer Society, 2009.
[98]肖燕彩,陈秀海,朱衡君.基于最小二乘向量机的变压器油中气体浓度预测[J].电网技术,2006,30 (11):91-94.
[99] Fei S W, Liu C L, Miao Y B. Support vector machine with genetic algorithm for forecasting of key-gas ratios in oil-immersed transformer[J]. Expert Systems with Applications,36(3):6326-6331.
[100]肖燕彩,陈秀海,朱衡君.以最小二乘向量机作组合器的变压器油中溶解气体体积分数预测[J].电力自动化设备,2008,28(7):33-36.
[101]熊和金,陈德军编著.智能信息处理[M].北京:国防工业出版社,2006.6:54-73,141-164,202-227, 244-247.
[102]王耀南.智能信息处理技术[M].北京:高等教育出版社,2003.8:1-31,284-326.
[103]高隽.智能信息处理方法[M].北京:机械工业出版社,2004.6:1-17,70-85.
[104]阎长顺,李一军.基于云模型的动态客户细分分类模型研究[J].哈尔滨工业大学学报,2007,39(2): 200-302.
[105]赵继印,郑蕊蕊,刘宇.基于梯形灰色聚类分析的电力变压器故障诊断[J].吉林大学学报(工学版), 2008,38(3):726-730.
[106]刘思峰,郭天榜,党耀国.灰色系统理论及其应用[M](第二版).北京:科学出版社,1999.
[107]苑津莎,鲁伟,李忠.基于人工免疫算法的变压器故障诊断方法[J].电子设计工程,2009,17(1): 46-48.
[108]余杰,周浩.变压器油气分析故障的免疫算法诊断模型[J].高电压术,2006,32(3):49-50,64.
[109]侯胜利,李应红,尉询楷.基于主元核相似度免疫机制的故障诊断方法及应[J].2006,27(2): 154-157,170.
[110]葛宏伟,李小琳,梁艳春,等.基于免疫粒子群优化的一种动态递归神经网络辨识与控制非线性系统[J].吉林大学学报(工学版),2008,38(4):858-864.
[111] Watkins A, Timms J, Boggess L. Artificial immune recognition systems(AIRS):an immune-inspired supervised learning algorithm[J]. Genetic Programming and Evolvable Machines,2004,5(3): 291-317.
[112]侯媛彬,杜京义,汪梅.神经网络[M].西安:西安电子科技大学出版社,2007.8:93-109,164-193.
[113]郑志勇.金融数量分析:MATLAB编程[M].北京:北京航空航天大学出版社,2009.11:152-165.
[114]雷英杰,张善文,李续武,等.MATLAB遗传算法工具箱及应用[M].西安:西安电子科技大学出社, 2005.4:1-207.
[115]吴宝春.基于遗传算法和灰色理论的电力变压器故障预测的研究[D].长春:吉林大学,2009.
[116]胡勇,程蕾.大型电力变压器故障实例统计分析[J].电力安全技术,2003,5(1):20-22.
[117]郑蕊蕊,赵继印,吴宝春,李建坡.基于加权灰靶理论的电力变压器绝缘状态分级评估方法[J].电工技术学报,2008,23(8):60-66.
[118]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,2005.
[119]程志华,章剑光.状态检修技术及其辅助分析系统的应用[J].电网技术,2003,27(7):16-24.
[120]中华人民共和国电力工业部.DL/T572-95电力变压器运行规程[S].北京:清华大学出版社,1996. 2.
[121]陈化钢.电力设备预防性试验方法及诊断技术[M].北京:中国科学技术出版社,2001.3.
[122]杨保初,刘晓波,戴玉松.高电压技术[M].重庆:重庆大学出版社,2001.12.
[123]中华人民共和国电力工业部.DL/T573-95电力变压器检修导则[S].1995,11.
[124]张启华.变压器油介损值增大的原因分析及油的净化处理[J].变压器,2007,44(1):75-76.
[125]郑海平,孙才新,李俭,等.诊断电力变压器故障的一种灰色关联度分析模式及方法[J].中国电机工程学报,2001,21(10):106-109.
[126]何雷,李丽平.基于云模型的电力变压器状态综合评判[J].华北电力大学学报,2009,36(3):98-103.
[127]孔英会,苑津莎,李新叶,等.基于时序特征和参数估计的变压器故障诊断方法[J].电工技术学报, 2008,23(12):48-54.
[128] Liang L M, Chen W G., Yue Y F, et al. Study on Power Transformers Fault Diagnosis Based on Wavelet Neural Network and D-S Evidence Theory[J]. High Voltage Engineering, 2008,34(12): 2694-2700.
[129]肖燕彩,陈秀海.改进的M-ary支持向量机模型及其在变压器故障诊断中的应用[J].上海交通大学学报,2008,42(12):2033-2036.
[130]李红芳,张清华,谢克明.一种新型免疫网络学习算法在故障诊断中的应用[J].智能系统学报, 2008,3(5):449-454.
[131]于繁华,刘寒冰.基于支持向量机和粒子群算法的结构损伤识别[J].吉林大学学报(工学版), 2008,38(2):434-438.
[132]孙晓亮,邵定宏.基于疫苗接种的免疫算法[J].计算机工程与设计,2007,28(16):3960-3962.
[133]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.
[134] Zheng R R, Zhao J Y, Wu B C. Transformer Oil Dissolved Gas Concentration Prediction Based on Genetic Algorithm and Improved Gray Verhulst Model [C].Proceedings of 2009 International Conference on Artificial Intelligence and Computational Intelligence,2009, November7-8,v4: 575-579. Piscataway:IEEE. Computer Society, 2009.
[135]赵文清,朱永利,张小奇.应用支持向量机的变压器故障组合预测[J].中国电机工程学报,2008,28 (5):14-19.
[136] Zheng R R, Zhao J Y, Wu B C. Transformer Fault Prediction based on GA and Variable Weight Gray Verhulst Model [C]. Proceedings of 2009 International Conference on Computational Intelligence and Software Engineering, 2009, December 11-13,v1:109-112. Piscataway:IEEE. Computer Society, 2009.
[137] Wang M H. Grey-extension method for incipient fault forecasting of oil-immersed power transformer[J].Electric Power Components and Syetems,2004,32(10):959-975.
[138]张克中,毛树华,袁卫红.马尔科夫残差修正灰色模型及其在公路网规划中的应用[J].武汉理工大学学报(交通科学与工程版),2005,29(4):503-505.
[139]王翠茹,孙辰军,杨静等.改进残差灰色预测模型在负荷预测中的应用[J].电力系统及其自动化学报,2006(18):86-89.
[140]常巍,谢光军,黄朝峰.MATLAB2007基础与提高[M].北京:电子工业出版社,2007.9:211-236.
[141]杨邵伟,韩天,尹忠俊,等.MATLAB GUI界面设计在电机故障诊断中的应用[J].机电产品开发与创新,2007,20(6):118-119,122.
[142]郑蕊蕊,赵继印,王志男,吴宝春.基于改进灰色聚类分析的电力变压器故障诊断[J].吉林大学学报(工学版),2008,38(5):1237-1241.
[143]肖慎勇,杨博,等.数据库及其应用(Access及Excel)[M].北京:清华大学出版社,2009.3:1-229.
[144]梁灿,赵艳铎.Access数据库应用基础教程[M].北京:清华大学出版社,2005.11:1-209.
[2] 2008年全国电力市场分析预测(秋季)报告[EB/OL].[2009-9-1]. http://www.chinasperi.com.cn/upload/attachment/200931152222177.pdf.
[3]中国电网发展情况[EB/OL].[2009-9-2].http://baike.baidu.com/view/69135.htm.
[4]我国电网建设实现大跨步前进[EB/OL].[2009-9-2]. http://info.electric.hc360.com/2007/10/10111460791.shtml.
[5]孙才新,陈伟根,李俭,等.电气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.
[6]中国供电可靠率达99.91%专家吁制订准入规则[EB/OL].[2009-9-2]. http://www.dtp-lc.com/m/tyxw/2008116232320.htm.
[7]南方电网公司发布2008年社会责任报告[EB/OL].[2009-9-2]. http://www.cnr.cn/newscenter/gnxw/200906/t20090605_505356963_1.html.
[8] Mizutani Y, Takahashi T, Ito T. Lifetime evaluation method for pole transformer based on transient temperature analysis[J]. IEEE Transactions on Power and Energy, 2007, 127(5):653-658.
[9] Zhao W Q , Zhu Y L. A prediction model for dissolved gas in transformer oil based on improved verhulst grey theory[C]. Proceedings of 3rd IEEE Conference on Industrial Electronics and Applications, 2008, June 3-5:2042-2044. Piscataway: IEEE. Computer Society, 2008.
[10] Sinqh A ,Verma P. A review of intelligent diagnostic methods for condition assessment of insulation system in power transformers[C]. Proceedings of 2008 International Conference on Condition Monitoring and Diagnosis, 2008, April 21-24:1354-1357. Piscataway: IEEE. Computer Society, 2007.
[11] [EB/OL].[2009-9-6]. http://159.226.244.28/portal/proj_search.asp.
[12] Morais R M , Mannheimer W A , Carballeira M , et al. Furfural analysis for assessing degradation of thermally upgraded papers in transformer insulation[J]. IEEE Transactions on Dielectrics and Electrical Insulation,1999,6(2):159-163.
[13]操敦奎.变压器油中气体分析诊断与故障检查[M].北京:中国电力出版社,2005.
[14] IEC. Interpretation of the analysis of gases in transformers and other oil-filled electrical equipment in service[S]. IEC Publication 599 (First Edition), 1978.
[15]中华人民共和国电力工业部.DL/T596-1996电力设备预防性试验规程[S].北京:中国电力出版社,1997.
[16]中华人民共和国国家经济贸易委员会.DL/T722-2000变压器油中溶解气体分析和判断导则[S].北京:中国电力出版社,2001.
[17]章政.基于遗传编程的电力变压器绝缘故障诊断模型研究[D].上海:上海交通大学,2007.
[18]李建坡.基于油中溶解气体分析的电力变压器故障诊断技术的研究[D].长春:吉林大学,2008.
[19]黄华,傅晨钊.大型电力变压器状态分析综述[J].华东电力,2004,32(3):23-26.
[20] Suwanasri T, Chaidee E, Adsoongnoen C. Failure statistics and power transformer condition evaluation by dissolved gas analysis technique[C]. Proceedings of 2008 International Conference on Condition Monitoring and Diagnosis,2008, April 21-24:492-496. Piscataway: IEEE. Computer Society, 2007.
[21]张小奇.变压器故障预报与状态评估的研究[D].保定:华北电力大学,2006.
[22]张成林,王克英.变电站设备状态检修现状分析及前景展望[C].中国高等学校电力系统及其自动化专业第二十二届学术会论文摘要集,2006.
[23]纪航.变压器状态综合评估方法研究[D].保定:华北电力大学,2005.
[24]廖玉祥.一种电力变压器运行状态综合评估模型的研究[D].重庆:重庆大学,2006.
[25] Fuhr J., Aschwanden T. Experience with Diagnostic Tools for Condition Assessment of Large Power Transformers[C]. IEEE International Symposium on Electrical Insulation, 2004, Septemper 19-22:508-511. Piscataway: IEEE,2004.
[26] Yadav R ,Kumar S ,Venkatasami A , et al. Condition based maintenance of power transformer: A case study[C]. Proceedings of 2008 International Conference on Condition Monitoring and Diagnosis, 2008, April 21-24:502-504. Piscataway: IEEE. Computer Society, 2007.
[27] Rusov V ,Zhivodernikov S. Transformer condition monitoring[C] .Proceedings of 2008 International Conference on Condition Monitoring and Diagnosis, 2008, April 21-24:1012-1014. Piscataway:IEEE. Computer Society, 2007.
[28] Muhr M , Sumereder C. Transformer condition determination methods[C]. Proceedings of the International Symposium on Electrical Insulating Materials,2008,Septermber 7-11:479-482. Tokyo: Institute of Electrical Engineers of Japan,2008.
[29]中国电力网.浙江省电力公司状态检修验收工作全面完成[WB/OL].(2008-10-06)[2009-9-7]. http://www.chinapower.com.cn/newsarticle/1075/new1075926.asp.
[30]国家电网.重庆公司状态检修工作通过国家电网公司验收[WB/OL].(2009-08-25)[2009-9-7]. http://www.sgcc.com.cn/xwzx/gsxw/2009/08/205006.shtml.
[31]陕西省电力公司.陕西省电力公司顺利通过国家电网公司状态检修工作验收[WB/OL].(2009-7-1)[2009-9-7].http://www.sxcoal.com/dlm/2009/07/01/470020/article.html.
[32]熊浩,孙才新,杜鹏,等.基于物元理论的电力变压器状态综合评估[J].重庆大学学报(自然科学版),2006,29(10):24-28.
[33]赵文清,朱永利.电力变压器状态评估综述[J].变压器,2007,44(11):9-12,74.
[34]吴莉琳,蔡玉广.分层式信息融合在变压器状态评估中的应用[J].广东电力,2007,20(2):29-32.
[35]廖瑞金,王谦,骆思佳,等.基于模糊综合评判的电力变压器运行状态评估模型[J].电力系统自动化,2008,32(3):70-75.
[36] Wang Y Y, Zhou J J,Chen W G., et al. Assessment method for the reliability of power transformer based on fault-tree analysis[J]. High Voltage Engineering,2009,35(3):514-520.
[37] Tang W H, Spurgeon K ,Wu Q H,et al. An evidential reasoning approach to transformer condition assessment [J]. IEEE TRANSACTIONS ON POWER DELIVERY,2004,19(4):1696-1703.
[38] Utami N Y, Tamsir Y, Pharmatrisanti A, et al. Evaluation condition of transformer based on infrared thermography results[C]. Proceedings of the IEEE International Conference on Properties and Applications of Dielectric Materials,2009, July 19-23:1055-1058. Piscataway: Institute of Electrical and Electronics Engineers Inc.,2009.
[39] Xie Q, Li Y Q, Xie H.L., et al. Large power transformer condition evaluation based on multilevel extension theory[C]. Proceedings of 3rd International Conference on Deregulation and Restructuring and Power Technologies,2008, April 6-9:933-936. Piscataway:IEEE. Computer Society, 2008.
[40] Wei J L, Zhang G. J, Dong M ,et al. Research progress on insulation condition estimation for oil-immersed power transformer[C]. Proceedings of the International Symposium on Electrical Insulating Materials,2008, September 7-11:32-35.Tokyo: Institute of Electrical Engineers ofJapan,2008.
[41] Németh B, Laboncz S, Kiss I. Condition monitoring of power transformers using DGA and fuzzy logic[C]. Proceedings of 2009 IEEE Electrical Insulation Conference,2009, May 31-June 3:373-376. Piscataway:IEEE. Computer Society, 2009.
[42]熊浩,孙才新,张昀,等.电力变压器运行状态的灰色层次评估模型[J].电力系统自动化,2007,31 (7):55-60.
[43]徐敏,钟文慧,刘井萍.基于模糊理论的变压器状态评估[J].南昌大学学报(工学版),2009,31(1): 53-56.
[44]袁志坚,孙才新,袁张渝,等.变压器健康状态评估的灰色聚类决策方法[J].重庆大学学报(自然科学版),2005,28(3):22-25.
[45]罗逸,邓聚龙,郑家粲,等.腐蚀评估中的灰靶方法[J].中国腐蚀与防护学报,2001,21(6):374-378.
[46]陈士玮,李柱国,徐启圣.基于灰靶理论和油液监测的磨损模式识别研究[J].中国矿业大学学报, 2005,34(3):353-357.
[47]范体军.基于灰靶理论的设备故障模式识别[J].中国设备工程,2004,(3):10-13.
[48]朱永利,申涛,李强.基于支持向量机和DGA的变压器状态评估方法[J].电力系统及其自动化学报,2008,20(6):111-115.
[49]申涛,朱永利,李强,等.基于支持向量机和DGA的变压器状态评估方法[J].电力科学与程,2008, 24(2):47-50.
[50] Hu H S, Qian S X, Wang J, et al. Application of information fusion technology in the remote state on-line monitoring and fault diagnosing system for power transformer[C]. Proceedings of 8th International Conference on Electronic Measurement and Instruments, 2007, August 16-18: 3550-3555. Piscataway:IEEE. Computer Society, 2007.
[51]谢红玲,律方成.基于信息融合的变压器状态评估方法研究[J].华北电力大学学报,2006,33(3): 8-11.
[52]郑蕊蕊.基于灰色系统理论的电力变压器故障诊断技术[D].长春:吉林大学大学,2007.
[53]章政,杨荆林,肖登明等.基于油中溶解气体分析的变压器绝缘故障诊断方法的研究和发展[J].电力设备,2004,5(1):20-24.
[54] Kalavathi M S, Reddy B R, Singh B P. Transformer fault diagnosis using fuzzy logic and neural network[C]. Annual Report-Conference on Electrical Insulation and Dielectric Phenomena,2005, October 16-19:486-489. Piscataway:IEEE. Computer Society, 2005.
[55] Wu T, Tu G.Y, Bo Z Q, et al. Fuzzy set theory and fault tree analysis based method suitable for fault diagnosis of power transformer[J]. International Conference on Intelligent Systems Applications to Power Systems,2007,1-5.
[56] Zheng X X. Intelligent fault diagnosis of power transformer based on fuzzy logic and rough set theory[C]. Proceedings of the World Congress on Intelligent Control and Automation,2008, June 25-27:6852-6857. Piscataway:IEEE. Computer Society, 2008.
[57] Purkait P,Chakravorti S. Time and frequency domain analysis based expert system for impulse fault diagnosis in transformers[J]. IEEE Transactions on Dielectrics and Electrical Insulation,2002,9(3): 433-445.
[58]苏文辉,鞠平.模糊专家系统在电力变压器在线故障诊断中的应用初探[J].电力系统及其自动化,2002,14(1):12-14.
[59] Guo Y.J.,Sun L. H.,Liang Y.C.,et al. The fault diagnosis of power transformer based on improved RBF neural network[C]. Proceedings of the Sixth International Conference on Machine Learningand Cybernetics, 2007, August 19-22,v2:1111-1114. Piscataway:IEEE. Computer Society, 2007.
[60] Georgilakis P S, Katsigiannis J A, Valavanis K P, et al. A systematic stochastic Petri net based methodology for transformer fault diagnosis and repair actions[J]. Journal of Intelligent and Robotic Systems: Theory and Applications, 2006, 45(2):181-201.
[61] Li C. Qian H., Yang X H, et al. The fault diagnosis of power transformer using clustering and radial basis function neural network[C]. Proceedings of the 2009 International Conference on Machine Learning and Cybernetics,2009, July 12-15,v3: 1257-1260. Piscataway:IEEE. Computer Society, 2009.
[62]王伟.油浸式电力变压器故障诊断技术的研究[D].济南:山东大学,2008.
[63] Li P , LüF C, Li N Y, et al. Study on fault diagnosis for power transformer based on cloud matter element analysis principle and DGA[C]. Proceedings of the IEEE International Conference on Properties and Applications of Dielectric Materials, 2009, July 19-23:244-248. Piscataway:IEEE., 2009.
[64] Shu H C, Hu Z J, Sun S Y, et al. The fault diagnosis algorithm for transformer based on Extenics and rough set theory [C]. Proceeding of 3rd International Conference on Deregulation and Restructuring and Power Technologies, 2008, April6-April9:1269-1272. Piscataway:IEEE. Computer Society, 2008.
[65] Wu Z L, Zhang B, ZhuY L, et al. Transformer fault portfolio diagnosis based on the combination of the multiple Bayesian classifier and SVM[C]. Proceedings of 2009 International Conference on Electronic Computer Technology, 2009, February 20-22:379-382. Piscataway:IEEE. Computer Society, 2009.
[66] Liu X,Dong D C,Wan G. C. Global fault diagnosis method of traction transformer based on improved fuzzy cellular neural network[C]. 4th IEEE Conference on Industrial Electronics and Applications,2009, May25-27:353-357. Piscataway:IEEE. Computer Society, 2009.
[67] Song B P, Chen M B. Power transformer DGA integrated diagnosis system based on Oracle database[C]. 2009 Asia-Pacific Power and Energy Engineering Conference, 2009, March 27-31. Piscataway:IEEE. Computer Society, 2009.
[68]陆静.蚁群算法在变压器故障诊断中的应用研究[D].南京:南京航空航天大学,2008.
[69]杨淑莹.模式识别与智能计算:Matlab实现[M].北京:电子工业出版社,2008.1:133-139,298-318.
[70] Li J S, Zhao X F, Sun Z Q, et al. The application of chaos support vector machines in transformer fault diagnosis[C]. Proceedings of the IEEE International Conference on Properties and Applications of Dielectric Materials,2009, July19-23:236-239. Piscataway:IEEE., 2009.
[71] Zhu Y L, Zheng J B, Wang F. Transformer fault diagnosis based on Euclidean clustering and support vector machines [C]. Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition,2007, November 2-4,v4:1453-1456. Piscataway:IEEE., 2007.
[72]蒋延军,倪远平.基于粗糙集与支持向量机的变压器故障诊断法[J].高电压技术,2008,34(80): 1755-1760.
[73] Zang H Z, Yu X D. Transform fault diagnosis utilizing rough set and support vector machine[C]. 2009 Asia-Pacific Power and Energy Engineering Conference,2009, March 27-31. Piscataway:IEEE. Computer Society, 2009.
[74]焦李成,杜海峰,刘芳,等著.免疫优化计算、学习与识别[M].北京:科学出版社,2006:1-39,45-46.
[75]邓晓刚,田学民.基于免疫核主元分析的故障诊断方法[J].清华大学学报(自然科学版),2008,48 (S2):1794-1798.
[76]刘欣,黄席樾,李伟,等.基于免疫原理的多Agent故障诊断系统[J].计算机仿真,2006,23(9): 153-156,201.
[77]熊浩,孙才新,陈伟根,等.电力变压器故障诊断的人工免疫网络分类算法[J].电力系统自动化, 2006,30(6):57-60.
[78]周爱华,张彼德,张厚宣.基于人工免疫分类算法的电力变压器故障诊断[J].高电压技术,2007,33 (8):77,87,97,08.
[79]李红芳,张清华,谢克明.一种新型免疫网络学习算法在故障诊断中的应用[J].智能系统学报,2008,3(5):449-454.
[80]彭媛,张春良,赵辉,等.基于人工免疫系统的核动力设备故障诊断[J].核动力工程,2008,29(2): 124-128.
[81]宋绍民,王耀南,孙炜,等.基于免疫聚类算法的变压器DGA数据故障诊断方法[J].变压器,2008, 45(8):70-72
[82]刘思峰.灰色系统理论的产生、发展及前沿动态[J].浙江万里学院学报,2003,16(4):14-17.
[83]邓聚龙著.灰理论基础[M].武汉:华中科技大学出版社,2002.2:1-307.
[84] Dong M, Yan Z,Yasuhiko T. Fault diagnosis of power transformer based on model-diagnosis with grey relation[C]. Proceedings of the International conference on properties and applications of dielectric materials,2003, June1-5:1158-1161. Piscataway:IEEE., 2003.
[85]李建坡,赵继印,郑蕊蕊.基于综合关联度分析的电力变压器故障诊断[J].吉林大学学报(信息科学版)2008,26(1):62-68.
[86]李俭,孙才新,陈伟根等.基于灰色聚类分析的充油电力变压器绝缘故障诊断的研究[J].电工技术学报,2002,17(4):24-29.
[87]柳纲,郭基伟,唐国庆.基于灰色位势理论的变压器故障诊断算法[J].电力系统及其自动化学报, 2004,16(2):54-57.
[88]曹建,范竞敏,丁家峰,等.改进的灰熵关联度算法用于变压器故障诊断[J].电力系统及其自动化学报,2009,21(4):25-29,87.
[89]伍伟慧.基于改进型灰色关联的变压器故障诊断[D].长沙:长沙理工大学,2009.
[90] Bochenski B, Mosinski F, Piotrowski T. Monitoring and forecasting of power transformer ratings using DTR computer program[C]. EUROCON 2007 - The International Conference on Computer as a Tool, 2007, September9-12:1373-1377. Piscataway:IEEE. Computer Society, 2007.
[91] Wang J, Liu J X, Wu L Z. Fault forecast of the transformer using new theory of grey system[C]. IET Conference Publications,2006, September17-20:340-345. Stevenage: Institution of Engineering and Technology,2006.
[92]孙才新,毕为民,周泉,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.
[93]肖燕彩,朱衡君,陈秀海.用灰色多变量模型预测变压器油中溶解的气体浓度[J].电力系统自动化,2006,30(13):64-67.
[94]费胜巍,孙宇.融合粗糙集与灰色理论的电力变压器故障预测[J].中国电机工程学报,2008,28 (16):154-160.
[95]杨廷方,刘沛,李浙,等.应用新型多方法组合预测模型估计变压器油中溶解气体浓度[J].中国电机工程学报,2008,28(31):108-113.
[96]乔俊玲.人工神经网络在变压器油中溶解气体分析中的应用研究[D].北京:北京交通大学,2007.
[97] Li W J,Zhang Y,Guo W Q, et al. Forecasting dissolved gases content in power transformer oil based on particle swarm optimization-based RBF neural network[C]. 2009 Second ISECS International Colloquium on Computing, Communication, Control, and Management,2009,August8-9,v3:153-155. Piscataway:IEEE. Computer Society, 2009.
[98]肖燕彩,陈秀海,朱衡君.基于最小二乘向量机的变压器油中气体浓度预测[J].电网技术,2006,30 (11):91-94.
[99] Fei S W, Liu C L, Miao Y B. Support vector machine with genetic algorithm for forecasting of key-gas ratios in oil-immersed transformer[J]. Expert Systems with Applications,36(3):6326-6331.
[100]肖燕彩,陈秀海,朱衡君.以最小二乘向量机作组合器的变压器油中溶解气体体积分数预测[J].电力自动化设备,2008,28(7):33-36.
[101]熊和金,陈德军编著.智能信息处理[M].北京:国防工业出版社,2006.6:54-73,141-164,202-227, 244-247.
[102]王耀南.智能信息处理技术[M].北京:高等教育出版社,2003.8:1-31,284-326.
[103]高隽.智能信息处理方法[M].北京:机械工业出版社,2004.6:1-17,70-85.
[104]阎长顺,李一军.基于云模型的动态客户细分分类模型研究[J].哈尔滨工业大学学报,2007,39(2): 200-302.
[105]赵继印,郑蕊蕊,刘宇.基于梯形灰色聚类分析的电力变压器故障诊断[J].吉林大学学报(工学版), 2008,38(3):726-730.
[106]刘思峰,郭天榜,党耀国.灰色系统理论及其应用[M](第二版).北京:科学出版社,1999.
[107]苑津莎,鲁伟,李忠.基于人工免疫算法的变压器故障诊断方法[J].电子设计工程,2009,17(1): 46-48.
[108]余杰,周浩.变压器油气分析故障的免疫算法诊断模型[J].高电压术,2006,32(3):49-50,64.
[109]侯胜利,李应红,尉询楷.基于主元核相似度免疫机制的故障诊断方法及应[J].2006,27(2): 154-157,170.
[110]葛宏伟,李小琳,梁艳春,等.基于免疫粒子群优化的一种动态递归神经网络辨识与控制非线性系统[J].吉林大学学报(工学版),2008,38(4):858-864.
[111] Watkins A, Timms J, Boggess L. Artificial immune recognition systems(AIRS):an immune-inspired supervised learning algorithm[J]. Genetic Programming and Evolvable Machines,2004,5(3): 291-317.
[112]侯媛彬,杜京义,汪梅.神经网络[M].西安:西安电子科技大学出版社,2007.8:93-109,164-193.
[113]郑志勇.金融数量分析:MATLAB编程[M].北京:北京航空航天大学出版社,2009.11:152-165.
[114]雷英杰,张善文,李续武,等.MATLAB遗传算法工具箱及应用[M].西安:西安电子科技大学出社, 2005.4:1-207.
[115]吴宝春.基于遗传算法和灰色理论的电力变压器故障预测的研究[D].长春:吉林大学,2009.
[116]胡勇,程蕾.大型电力变压器故障实例统计分析[J].电力安全技术,2003,5(1):20-22.
[117]郑蕊蕊,赵继印,吴宝春,李建坡.基于加权灰靶理论的电力变压器绝缘状态分级评估方法[J].电工技术学报,2008,23(8):60-66.
[118]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,2005.
[119]程志华,章剑光.状态检修技术及其辅助分析系统的应用[J].电网技术,2003,27(7):16-24.
[120]中华人民共和国电力工业部.DL/T572-95电力变压器运行规程[S].北京:清华大学出版社,1996. 2.
[121]陈化钢.电力设备预防性试验方法及诊断技术[M].北京:中国科学技术出版社,2001.3.
[122]杨保初,刘晓波,戴玉松.高电压技术[M].重庆:重庆大学出版社,2001.12.
[123]中华人民共和国电力工业部.DL/T573-95电力变压器检修导则[S].1995,11.
[124]张启华.变压器油介损值增大的原因分析及油的净化处理[J].变压器,2007,44(1):75-76.
[125]郑海平,孙才新,李俭,等.诊断电力变压器故障的一种灰色关联度分析模式及方法[J].中国电机工程学报,2001,21(10):106-109.
[126]何雷,李丽平.基于云模型的电力变压器状态综合评判[J].华北电力大学学报,2009,36(3):98-103.
[127]孔英会,苑津莎,李新叶,等.基于时序特征和参数估计的变压器故障诊断方法[J].电工技术学报, 2008,23(12):48-54.
[128] Liang L M, Chen W G., Yue Y F, et al. Study on Power Transformers Fault Diagnosis Based on Wavelet Neural Network and D-S Evidence Theory[J]. High Voltage Engineering, 2008,34(12): 2694-2700.
[129]肖燕彩,陈秀海.改进的M-ary支持向量机模型及其在变压器故障诊断中的应用[J].上海交通大学学报,2008,42(12):2033-2036.
[130]李红芳,张清华,谢克明.一种新型免疫网络学习算法在故障诊断中的应用[J].智能系统学报, 2008,3(5):449-454.
[131]于繁华,刘寒冰.基于支持向量机和粒子群算法的结构损伤识别[J].吉林大学学报(工学版), 2008,38(2):434-438.
[132]孙晓亮,邵定宏.基于疫苗接种的免疫算法[J].计算机工程与设计,2007,28(16):3960-3962.
[133]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.
[134] Zheng R R, Zhao J Y, Wu B C. Transformer Oil Dissolved Gas Concentration Prediction Based on Genetic Algorithm and Improved Gray Verhulst Model [C].Proceedings of 2009 International Conference on Artificial Intelligence and Computational Intelligence,2009, November7-8,v4: 575-579. Piscataway:IEEE. Computer Society, 2009.
[135]赵文清,朱永利,张小奇.应用支持向量机的变压器故障组合预测[J].中国电机工程学报,2008,28 (5):14-19.
[136] Zheng R R, Zhao J Y, Wu B C. Transformer Fault Prediction based on GA and Variable Weight Gray Verhulst Model [C]. Proceedings of 2009 International Conference on Computational Intelligence and Software Engineering, 2009, December 11-13,v1:109-112. Piscataway:IEEE. Computer Society, 2009.
[137] Wang M H. Grey-extension method for incipient fault forecasting of oil-immersed power transformer[J].Electric Power Components and Syetems,2004,32(10):959-975.
[138]张克中,毛树华,袁卫红.马尔科夫残差修正灰色模型及其在公路网规划中的应用[J].武汉理工大学学报(交通科学与工程版),2005,29(4):503-505.
[139]王翠茹,孙辰军,杨静等.改进残差灰色预测模型在负荷预测中的应用[J].电力系统及其自动化学报,2006(18):86-89.
[140]常巍,谢光军,黄朝峰.MATLAB2007基础与提高[M].北京:电子工业出版社,2007.9:211-236.
[141]杨邵伟,韩天,尹忠俊,等.MATLAB GUI界面设计在电机故障诊断中的应用[J].机电产品开发与创新,2007,20(6):118-119,122.
[142]郑蕊蕊,赵继印,王志男,吴宝春.基于改进灰色聚类分析的电力变压器故障诊断[J].吉林大学学报(工学版),2008,38(5):1237-1241.
[143]肖慎勇,杨博,等.数据库及其应用(Access及Excel)[M].北京:清华大学出版社,2009.3:1-229.
[144]梁灿,赵艳铎.Access数据库应用基础教程[M].北京:清华大学出版社,2005.11:1-209.