面向智能水电站的远程监测与分析系统
|
摘要
随着智能电网的发展,智能水电站的建设将是今后的主要发展方向和全新目标。状态监测与故障诊断是实现智能水电站的重要手段。水电站设备之间关系密切,相互耦合构成有机整体,设备分析与诊断需要关联相关设备状态,从全局进行考虑。为了全面准确地分析设备运行状态、评估设备健康状况,急需提供一个符合智能水电站要求的集成所有设备状态数据的一体化监测与分析平台。此外,让远程专家不用到现场通过网络就能实现设备分析与诊断,提高诊断效率,也是人们研究的热点。在对当前设备故障诊断技术以及水电站监测与分析系统研究现状进行总结的基础上,结合葛洲坝水电站的设备分析需求,通过理论与实践相结合,开展了面向智能水电站的远程监测与分析系统研究。
首先分析了水电站设备故障的特点。在总结运行人员工作职责以及进行设备运行分析流程与方法的基础上,提出了能够模拟专家自动完成的水电站智能化设备运行分析方法：状态统计,通过状态周期性运行统计以及同工况运行统计,为设备运行分析提供数据支持；运行分析,关联设备工况与相关状态,通过工况关联阈值分析、关联分析以及趋势分析,评价设备健康状况,检测设备性能降低或故障；事件分析,通过设备故障树,对性能降低或故障事件进行分析,列出故障嫌疑设备；设备分析,采用基于诊断知识规则、基于仿真模型、签名分析以及交互式诊断等诊断方法,分析设备故障,确诊故障原因。并研究了方法的自动实现过程。
分析了智能水电站的特征,介绍了水电站现有的监测与分析系统,包括监控系统、状态监测系统以及离线分析系统,研究了面向智能水电站系统集成的必要性。以机组集成状态监测系统为主体,通过信息共享、网络通信等技术,从现地层、厂站层以及企业层实现了各系统之间的集成,构建了水电站分层分布式一体化监测与分析系统,并采用时间同步、工况同步以及事件同步等手段,实现了各系统数据的有机融合与集成存储。根据系统层次结构的功能需求,研究了一体化系统的数据层次组织策略,介绍了数据内容与组织形式。分析了一体化系统的安全现状,采用安全分区、硬件隔离等策略,实现了系统安全防护,安全测试结果表明系统运行安全可靠。
针对目前远程监测与诊断还需要专家到现场进行操作的不足,提出了远程零距离监测与诊断思想,即不用到现场通过网络就能实现远程监测与诊断,就像亲临现场一样,实现专家与现场零距离。根据专家现场工作的需求分析,设计了包含远程零距离状态巡检、运行分析、试验分析以及故障诊断的功能框架。为了实现远程用户与系统的交互,建立了基于RIA的远程信息交换模型。采用设备状态数字化、可视化导航以及系统状态自检测等手段,将设备状态、系统状态展示给专家,实现远程状态巡检；通过设备状态统计,集成阈值分析、关联分析以及趋势分析,为专家提供远程运行分析；系统自动识别机组“试验”(包括人工试验及正常运行经历的工况)、记录试验数据、计算试验性能指标、评价试验性能并生成试验报告,实现远程试验分析；提供原始数据提取、立体数据查询、故障特征分析以及故障诊断等功能应用,使得专家在远程就能进行故障分析与诊断。详细阐述了数据库、知识库设计以及功能实现方法。
面向智能水电站的一体化远程零距离监测与分析系统已在葛洲坝水电站成功应用。结合实例,介绍了远程零距离监测、分析与诊断应用成果,验证了系统的可行性和实用性。水电站一体化远程零距离监测与分析系统为专家进行设备远程综合监测、分析、故障诊断以及维护决策提供了交互式信息平台,为运行人员进行运行分析、把握设备健康状况提供了强有力的辅助工具,为实现智能水电站打下了坚实的基础。
With the development of smart grid, smart hydro power station building will be the future direction of development and a new target.And condition monitoring and fault diagnosis is an important means to achieve smart hydro power station. The equipments of hydropower stations have close relationships between them, and they constitute an organic whole coupled with each other. So analysis and diagnosis for the equipments needs to associate with the related equipment status and to be considered from the global. In order to analyze the operating status and evaluate the health of the equipment fully and accurately, an integrated monitoring and analysis platform integrated with all equipment status data is needed urgently, which meets the requirements of smart hydro power station. In addition, the study that the analysis and diagnosis can be achieved through the network instead of going to the site for the remote experts to improve the diagnosis efficiency is a focus for the researchers. Based on the summary of the current research situation of the equipment fault diagnosis technique and systems, the dissertation is focused on research an integrated remote monitoring and analysis system for smart hydro power station. The research is combined with the analysis needs of Gezhouba Hydro Power Station. And the methodology and technique are comprehensively investigated on integration of theory with practice.
First, the characteristics of the equipment failure of hydro power station are analyzed. The work responsibilities and the procedures and methods of equipment operation analysis for the operating personnels are summarized. Then an intelligent equipment operation analysis method for the hydro power stations is present which can be performed automatically by simulating the experts. Status statistics provides data support for the equipment operaiotn analysis by periodical statistics and statistics in the same operation condition. Associated the operation condition and association status, the health status is evalutated and the performance degradation and failure is detected by operation analysis, which inclues the threshold analysis associated with the operation condition, association analysis and trend analysis. The performance degradation or failure event is analyzed by event analysis through fault trees and the fault suspect devices are list. Then the equipment failures are analyzed and the causes of the fault are confirmed by device analysis, which includes the diagnostic methods based knowledge rules, methods based simulation models, signature analysis as well as interactive diagnosis. The automatically realization of the method is studied.
The characteristics of smart hydro power stations are analyzed and the existing monitoring and analysis sytems in the hydropower station are introduced, including monitoring and control system, condition monitoring systems as well as off-line analysis systems. The integration necessity of the systems for smart hydro power stations are studied. Taken the integration condition monitoring system as the main, various systems integration is realized from the site layer, the plant layer and the enterprise layer through information sharing, networking communications technologies. And an integrated hierarchical and distributed monitoring and analysis system is constituted. And time synchronization, operation condition synchronization and event synchronization means are adopted to achieve the organic integration and comprenhensive storage for the data of the system. According to the functional requirements of the system, the data hierarchy organizational strategy is studied and the data content and forms of organization are introduced. Then the safe status of the integrated system is analyzed. And the system security defence is carried out by the means of safe partition, hardware isolation,. The security test results show that the system is safe and reliable.
For the current remote monitoring and analysis systems, it needs the experts to go to the site to operate. The idea of remote zero distance monitoring and diagnosis is proposed, that is, remote monitoring and diagnosis can be achieved through the network instead of going to the site for the experts, like experiencing on the site. And the zero distance is realized between the experts and the site. According to the needs analysis of the work on-stie, the function framework is designed, which contains remote zero distance status inspection, operation analysis, test analysis and fault diagnosis. In order to achieve the interaction between the remote uses and the system, the remote information exchange model based RIA is established. By means of equipment status digitization, visualization navigation as well as system status detection, the equipment and system status are displayed to the remote experts and the remote status inspection is realized. Remote operation analysis integrated threshold analysis, association analysis and trend analysis is provided for the experts through equipments status statistics. The system recognizes the test of the unit(manual test and the operation condition experienced in normal operation) and the test data are stored, then the test performance indicators are calculated and the test performance is evaluated, at last the test reports are generated automatically. So the remote test analysis is carried out. Besides, the tools of raw data extraction, three-dimensional data queries, fault characteristics analysis as well as fault diagnosis are provided, and the experts on the remote will be able to perform fault diagnosis. The database and knowledge base as well as the realization methods of the function are introduced in detail.
The integrated remote zero distance monitoring and analysis system for the smart hydro power stations has been applied in Gezhouba Hydro Power Station successfully. Combined with the examples, the application achievement of remote zero distance monitoring, analysis and diagnosis are demonstrated, which proves the feasibility and practicability of the system. The system provides an interactive information platform for the expert to perform the monitoring, analysis, fault diagnosis and maintenance decision-making of the equipment. It also provides a powerful tool for the operating pesonnels to run the operation analysis and to grasp the health condition of the equipment. All the achievements laid a solid foundation to achieve smart hydropower stations.
首先分析了水电站设备故障的特点。在总结运行人员工作职责以及进行设备运行分析流程与方法的基础上,提出了能够模拟专家自动完成的水电站智能化设备运行分析方法：状态统计,通过状态周期性运行统计以及同工况运行统计,为设备运行分析提供数据支持；运行分析,关联设备工况与相关状态,通过工况关联阈值分析、关联分析以及趋势分析,评价设备健康状况,检测设备性能降低或故障；事件分析,通过设备故障树,对性能降低或故障事件进行分析,列出故障嫌疑设备；设备分析,采用基于诊断知识规则、基于仿真模型、签名分析以及交互式诊断等诊断方法,分析设备故障,确诊故障原因。并研究了方法的自动实现过程。
分析了智能水电站的特征,介绍了水电站现有的监测与分析系统,包括监控系统、状态监测系统以及离线分析系统,研究了面向智能水电站系统集成的必要性。以机组集成状态监测系统为主体,通过信息共享、网络通信等技术,从现地层、厂站层以及企业层实现了各系统之间的集成,构建了水电站分层分布式一体化监测与分析系统,并采用时间同步、工况同步以及事件同步等手段,实现了各系统数据的有机融合与集成存储。根据系统层次结构的功能需求,研究了一体化系统的数据层次组织策略,介绍了数据内容与组织形式。分析了一体化系统的安全现状,采用安全分区、硬件隔离等策略,实现了系统安全防护,安全测试结果表明系统运行安全可靠。
针对目前远程监测与诊断还需要专家到现场进行操作的不足,提出了远程零距离监测与诊断思想,即不用到现场通过网络就能实现远程监测与诊断,就像亲临现场一样,实现专家与现场零距离。根据专家现场工作的需求分析,设计了包含远程零距离状态巡检、运行分析、试验分析以及故障诊断的功能框架。为了实现远程用户与系统的交互,建立了基于RIA的远程信息交换模型。采用设备状态数字化、可视化导航以及系统状态自检测等手段,将设备状态、系统状态展示给专家,实现远程状态巡检；通过设备状态统计,集成阈值分析、关联分析以及趋势分析,为专家提供远程运行分析；系统自动识别机组“试验”(包括人工试验及正常运行经历的工况)、记录试验数据、计算试验性能指标、评价试验性能并生成试验报告,实现远程试验分析；提供原始数据提取、立体数据查询、故障特征分析以及故障诊断等功能应用,使得专家在远程就能进行故障分析与诊断。详细阐述了数据库、知识库设计以及功能实现方法。
面向智能水电站的一体化远程零距离监测与分析系统已在葛洲坝水电站成功应用。结合实例,介绍了远程零距离监测、分析与诊断应用成果,验证了系统的可行性和实用性。水电站一体化远程零距离监测与分析系统为专家进行设备远程综合监测、分析、故障诊断以及维护决策提供了交互式信息平台,为运行人员进行运行分析、把握设备健康状况提供了强有力的辅助工具,为实现智能水电站打下了坚实的基础。
With the development of smart grid, smart hydro power station building will be the future direction of development and a new target.And condition monitoring and fault diagnosis is an important means to achieve smart hydro power station. The equipments of hydropower stations have close relationships between them, and they constitute an organic whole coupled with each other. So analysis and diagnosis for the equipments needs to associate with the related equipment status and to be considered from the global. In order to analyze the operating status and evaluate the health of the equipment fully and accurately, an integrated monitoring and analysis platform integrated with all equipment status data is needed urgently, which meets the requirements of smart hydro power station. In addition, the study that the analysis and diagnosis can be achieved through the network instead of going to the site for the remote experts to improve the diagnosis efficiency is a focus for the researchers. Based on the summary of the current research situation of the equipment fault diagnosis technique and systems, the dissertation is focused on research an integrated remote monitoring and analysis system for smart hydro power station. The research is combined with the analysis needs of Gezhouba Hydro Power Station. And the methodology and technique are comprehensively investigated on integration of theory with practice.
First, the characteristics of the equipment failure of hydro power station are analyzed. The work responsibilities and the procedures and methods of equipment operation analysis for the operating personnels are summarized. Then an intelligent equipment operation analysis method for the hydro power stations is present which can be performed automatically by simulating the experts. Status statistics provides data support for the equipment operaiotn analysis by periodical statistics and statistics in the same operation condition. Associated the operation condition and association status, the health status is evalutated and the performance degradation and failure is detected by operation analysis, which inclues the threshold analysis associated with the operation condition, association analysis and trend analysis. The performance degradation or failure event is analyzed by event analysis through fault trees and the fault suspect devices are list. Then the equipment failures are analyzed and the causes of the fault are confirmed by device analysis, which includes the diagnostic methods based knowledge rules, methods based simulation models, signature analysis as well as interactive diagnosis. The automatically realization of the method is studied.
The characteristics of smart hydro power stations are analyzed and the existing monitoring and analysis sytems in the hydropower station are introduced, including monitoring and control system, condition monitoring systems as well as off-line analysis systems. The integration necessity of the systems for smart hydro power stations are studied. Taken the integration condition monitoring system as the main, various systems integration is realized from the site layer, the plant layer and the enterprise layer through information sharing, networking communications technologies. And an integrated hierarchical and distributed monitoring and analysis system is constituted. And time synchronization, operation condition synchronization and event synchronization means are adopted to achieve the organic integration and comprenhensive storage for the data of the system. According to the functional requirements of the system, the data hierarchy organizational strategy is studied and the data content and forms of organization are introduced. Then the safe status of the integrated system is analyzed. And the system security defence is carried out by the means of safe partition, hardware isolation,. The security test results show that the system is safe and reliable.
For the current remote monitoring and analysis systems, it needs the experts to go to the site to operate. The idea of remote zero distance monitoring and diagnosis is proposed, that is, remote monitoring and diagnosis can be achieved through the network instead of going to the site for the experts, like experiencing on the site. And the zero distance is realized between the experts and the site. According to the needs analysis of the work on-stie, the function framework is designed, which contains remote zero distance status inspection, operation analysis, test analysis and fault diagnosis. In order to achieve the interaction between the remote uses and the system, the remote information exchange model based RIA is established. By means of equipment status digitization, visualization navigation as well as system status detection, the equipment and system status are displayed to the remote experts and the remote status inspection is realized. Remote operation analysis integrated threshold analysis, association analysis and trend analysis is provided for the experts through equipments status statistics. The system recognizes the test of the unit(manual test and the operation condition experienced in normal operation) and the test data are stored, then the test performance indicators are calculated and the test performance is evaluated, at last the test reports are generated automatically. So the remote test analysis is carried out. Besides, the tools of raw data extraction, three-dimensional data queries, fault characteristics analysis as well as fault diagnosis are provided, and the experts on the remote will be able to perform fault diagnosis. The database and knowledge base as well as the realization methods of the function are introduced in detail.
The integrated remote zero distance monitoring and analysis system for the smart hydro power stations has been applied in Gezhouba Hydro Power Station successfully. Combined with the examples, the application achievement of remote zero distance monitoring, analysis and diagnosis are demonstrated, which proves the feasibility and practicability of the system. The system provides an interactive information platform for the expert to perform the monitoring, analysis, fault diagnosis and maintenance decision-making of the equipment. It also provides a powerful tool for the operating pesonnels to run the operation analysis and to grasp the health condition of the equipment. All the achievements laid a solid foundation to achieve smart hydropower stations.
引文
[1]卢永,甘德强,Jiang John N.美国智能电网和分布式发电重点方向的调研分析[J].电力系统自动化,2010(9)：12-16.
[2]倪敬敏,何光宇,沈沉,等.美国智能电网评估综述[J].电力系统自动化,2010(8)：9-13.
[3]潘家才,纪浩.智能水电站建设思路[J].水电自动化与大坝监测,2012(1)：1-4.
[4]王益民.坚强智能电网技术标准体系研究框架[J].电力系统自动化,2010(22)：1-6.
[5]冯汉夫,石爽,马琴,等.智能化水电站建设的思考[J].水电厂自动化,2010(4)：1-5.
[6]刘观标,李晓斌,李永红,等.智能水电厂的体系结构[J].水电自动化与大坝监测,2011(1)：1-4.
[7]王海.水轮发电机组状态检修技术[M].武汉：中国电力出版社,2004.
[8]黄树红,李建兰.发电设备状态检修与诊断方法[M].北京：中国电力出版社,2008.
[9]杨兴斌.水电厂技术资料与专家知识数字化方法研究与应用[博士学位论文].武汉：华中科技大学,2009.
[10]虞和济.故障诊断的基本原理[M].北京：冶金工业出版社,1989.
[11]魏晓宾,马小平,李亚朋.故障诊断技术综述[J].煤矿机电,2009(1)：63-65.
[12]彭文季.水电机组振动故障的智能诊断方法研究[博士学位论文].西安：西安理工大学,2007.
[13]张栓柱.小波分析在异步电机故障诊断中的应用研究[J].电机技术,2011(6)：37-40.
[14]裴新才,许同乐.小波分析在轴承故障特征信号降噪中的应用[J].山东理工大学学报(自然科学版),2011(2)：89-91.
[15]Shi J Z, Gu F, Goulding P, et al. Integration of multiple platforms for real-time remote model-based condition monitoring[J]. Computers in Industry,2007,58(6):531-538.
[16]Simani S, Fantuzzi C. Dynamic system identification and model-based fault diagnosis of an industrial gas turbine prototype[J]. Mechatronics,2006,16(6):341-363.
[17]Isermann R. Model-based fault-detection and diagnosis-status and applications [J]. Annual Reviews in Control,2005,29(1):71-85.
[18]Cunningham P. A case study on the use of model-based systems for electronic fault diagnosis[J]. Artificial Intelligence in Engineering,1998,12(3):283-295.
[19]金鑫,任献彬,周亮.智能故障诊断技术研究综述[J].国外电子测量技术,2009(7)：30-32.
[20]陈启卷,张军仿,张超.基于故障树的抽水蓄能电站球阀故障诊断研究[J].水力发电,2010(5)：50-52.
[21]洪冶,蔡维由,乐振春.模糊故障树诊断及应用[J].武汉大学学报(工学版),2001(1)：93-94.
[22]赵新泽,杨明松,彭巍,等.基于故障树分析的闸门双吊点液压启闭系统故障诊断研究[J].水力发电,2011(8)：68-70.
[23]Chang S, Lin C, Chang C. A fuzzy diagnosis approach using dynamic fault trees[J]. Chemical Engineering Science,2002,57(15):2971-2985.
[24]Duan R, Zhou H. A New Fault Diagnosis Method Based on Fault Tree and Bayesian Networks[J]. Energy Procedia,2012,17, Part B(1):1376-1382.
[25]彭文季,罗兴錡.基于小波神经网络的水电机组振动故障诊断研究[J].水力发电学报,2007(1)：123-128.
[26]杨淑红.基于小波神经网络火电厂锅炉故障诊断研究[J].煤炭技术,2011(10)：46-48.
[27]Jia Y, K. T C, Xiangsheng W, et al. Modelling of chiller performance using artificial neural networks[J]. Energy Procedia,2011,13(3):1011-1016.
[28]Saxena A, Saad A. Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems[J]. Applied Soft Computing,2007,7(1):441-454.
[29]Rajakarunakaran S, Venkumar P, Devaraj D, et al. Artificial neural network approach for fault detection in rotary system[J]. Applied Soft Computing,2008,8(1):740-748.
[30]符向前,蒋劲,孙慕群,等.水电机组故障诊断系统中的模糊诊断技术研究[J].华中科技大学学报(自然科学版),2006(1)：81-83.
[31]杨志荣,周建中.基于模糊理论和多智能体的水电机组在线故障诊断研究[J].水力发电,2010(3)：72-74.
[32]陈铁华,陈启卷.模糊聚类分析在水电机组振动故障诊断中的应用[J].中国电机工程学报,2002(3)：44-48.
[33]Sakthivel N R, Sugumaran V, Nair B B. Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump[J]. Mechanical Systems and Signal Processing,2010,24(6):1887-1906.
[34]Han Y, Li Y, Chen Q, et al. A Cooperative Spectrum Sensing Algorithm Based on Fuzzy set and D-S Theory[J]. Energy Procedia,2011,13(5):8376-8382.
[35]Ye J. Fault diagnosis of turbine based on fuzzy cross entropy of vague sets[J]. Expert Systems with Applications,2009,36(4):8103-8106.
[36]张双全,袁晓辉.水电机组在线监测技术与故障诊断专家系统[J].水力发电,2003(7)：43-45.
[37]陈卫钢,周建中,常黎.基于专家系统的水电机组振动故障诊断研究[J].华中科技大学学报(自然科学版),2002(6)：102-104.
[38]Wu J, Bai M R, Su F, et al. An expert system for the diagnosis of faults in rotating machinery using adaptive order-tracking algorithm[J]. Expert Systems with Applications,2009,36(3, Part 1):5424-5431.
[39]Qian Y, Xu L, Li X, et al. LUBRES:An expert system development and implementation for real-time fault diagnosis of a lubricating oil refining process[J]. Expert Systems with Applications,2008,35(3):1252-1266.
[40]Zhi-Ling Y, Bin W, Xing-Hui D, et al. Expert System of Fault Diagnosis for Gear Box in Wind Turbine[J]. Systems Engineering Procedia,2012,4(3):189-195.
[41]Tang J, Wang Q. Online fault diagnosis and prevention expert system for dredgers[J]. Expert Systems with Applications,2008,34(1):511-521.
[42]Wu J, Liu C. An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network[J]. Expert Systems with Applications,2009,36(3, Part 1): 4278-4286.
[43]Li J, Shen S. Research on the Algorithm of Avionic Device Fault Diagnosis Based on Fuzzy Expert System[J]. Chinese Journal of Aeronautics,2007,20(3):223-229.
[44]Yang B, Lim D, Tan A C C. VIBEX:an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table[J]. Expert Systems with Applications,2005, 28(4):735-742.
[45]吴炜,陈喜阳.基于小波——神经网络的水电机组远程监测诊断系统[J].湖北电力,2005(4)：3-4.
[46]梁武科,赵道利,马薇,等.基于粗糙集-RBF神经网络的水电机组故障诊断[J].仪器仪表学报,2007(10)：1806-1810.
[47]张孝远,周建中,黄志伟,等.基于粗糙集和多类支持向量机的水电机组振动故障诊断[J].中国电机工程学报,2010(20)：88-93.
[48]安学利,周建中,刘力,等.基于熵权理论和信息融合技术的水电机组振动故障诊断[J].电力系统自动化,2008(20)：78-82.
[49]彭兵,周建中,方仍存,等.基于开机过程信息融合的水电机组故障诊断方法[J].电力系统自动化,2008(13)：76-80.
[50]赵道利,马薇,梁武科,等.水电机组振动故障的信息融合诊断与仿真研究[J].中国电机工程学报,2005(20)：137-142.
[51]彭文季,郭鹏程,罗兴錡.基于最小二乘支持向量机和信息融合技术的水电机组振动故障 诊断研究[J].水力发电学报,2007(6)：137-142.
[52]彭兵.基于改进支持向量机和特征信息融合的水电机组故障诊断[博士学位论文].武汉：华中科技大学,2008.
[53]李郁侠,刘立峰,陈继尧,等.基于神经网络和证据理论融合的水电机组振动故障诊断研究[J].西北农林科技大学学报(自然科学版),2005(10)：115-119.
[54]刘立峰,李郁侠,王伟.基于遗传神经网络和证据理论融合的水电机组振动故障诊断研究[J].水力发电学报,2008(5)：163-167.
[55]梁武科,赵道利,王荣荣,等.水电机组振动故障的粗糙集-神经网络诊断方法[J].西北农林科技大学学报(自然科学版),2007(7)：223-226.
[56]梁武科,赵道利,马薇,等.基于粗糙集-RBF神经网络的水电机组故障诊断[J].仪器仪表学报,2007(10)：1806-1810.
[57]Wu J, Kuo J. An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network[J]. Expert Systems with Applications,2009,36(6):9776-9783.
[58]Saravanan N, Siddabattuni V N S K, Ramachandran K I. Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM)[J]. Applied Soft Computing,2010,10(1):344-360.
[59]余文宁.大型水轮发电机组状态监测与智能故障诊断系统研究[硕士学位论文].长沙：中南大学,2007.
[60]于晶.DCS-分散控制系统及应用现状[J].工业仪表与自动化装置,1993(4)：43-47.
[61]宋天相,王义.恩泰克预测维修系统在船舶诊断分析中的应用[C].见：95全国设备故障诊断技术学术论文集.武汉,1995：1014-1018
[62]严可国,魏克严,李植.大型旋转机械监测保护故障诊断系统[M].北京：美国本特利内华达公司北京办事处,2002.
[63]S B J, G P A. Plant vibration monitoring at loviisa nuclear power plant[J]. Noise and vibration, 1991,22(8):34-37.
[64]Benati, C F, C S G. Vibration monitoring and mechanical diagnosis on large rotating machinery in ENEL power plants [J]. Symposium on Diagnostics Rotating Machines in Power Plants, 1993(10):27-29.
[65]Tanaka M. The diagnosis technologies in power plant in Japan[J]. Symposium on Diagnostics Rotating Machines in Power Plants,1993:27-29.
[66]王正旭,邵先荣.克拉斯诺雅尔斯克水电站设备的诊断[J].水利水电快报,2003,24(19)：5-8.
[67]李辉.基于粗糙集的水轮发电机组振动故障诊断系统研究[硕士学位论文].西安：西安理工大学,2006.
[68]刘峰.基于神经网络的水轮发电机组振动故障诊断专家系统的研究[硕士学位论文].西安：西安理工大学,2003.
[69]张彦宁.基于小波分析的水电机组在线监测与故障诊断的研究[硕士学位论文].西安：西安理工大学,2003.
[70]余涛,王晶,高峰,等.水电机组故障诊断专家系统研究现状与发展前景[J].云南电力技术,1999(2)：52-55.
[71]朱燕.水轮机组状态监测及故障分析系统研究开发[硕士学位论文].南京：东南大学,2007.
[72]王宇珩,徐立新,戴亚平,等.水电机组旋转机械故障诊断专家系统研究[J].新技术新工艺,2003(4)：7-9.
[73]褚福磊,卢文秀,张伟,等.水泵水轮机组状态监测与故障诊断系统[J].水力发电,1999(2)：31-33.
[74]沈文辉,陈国栋.水电厂机组状态监测系统与计算机监控系统的数据共享[J].水电自动化与大坝监测,2004(5)：7-10.
[75]任乐鸣,李文清.机组状态监测与故障诊断系统在紧水滩水电厂的应用[J].水电能源科学, 2006(2)：94-96.
[76]张云峰.密云水电厂监控系统技术改造[J].水电厂自动化,2005(1)：181-184.
[77]全新建,朱辰,施冲,等.基于三峡电站监控系统的状态监测趋势分析系统[J].水电厂自动化,2005(1)：138-142.
[78]曹锋.水轮发电机组诊断方法研究及信息平台设计[硕士学位论文].武汉：华中科技大学,2003.
[79]艾友忠.葛洲坝电厂最优维护方法研究与实践[博士学位论文].武汉：华中科技大学,2007.
[80]董毓新.水轮发电机组振动[M].辽宁：大连理工大学出版社,1989.
[81]马震岳,董毓新.水轮发电机组动力学[M].辽宁：大连理工出版社,2004.
[82]史会轩.大型水轮机空化在线监测与分析方法及应用研究[博士学位论文].武汉：华中科技大学,2008.
[83]陈燚涛.水轮机调速系统优化维护理论与实践[博士学位论文].华中科技大学,2005.
[84]何建明.智能电网信息和通信技术关键问题探讨[J].企业技术开发,2012(1)：32-34.
[85]高勤.NARI-NC2000计算机监控系统在葛洲坝二江电厂的应用[J].水电厂自动化,2008(4)：15-19.
[86]张家治.NC2000计算机监控系统在葛洲坝电厂的应用[J].水电厂自动化,2008(3)：20-21.
[87]易发德.葛洲坝大江厂计算机监控系统[J].华中电力,1994(6)：49-55.
[88]郭江,林霖,曹禹,等.基于Web的水电站大坝巡检系统设计与开发[J].水电能源科学,2010(7)：54-56.
[89]韩长利,仇明,李智.用油色谱分析方法检测变压器故障[J].变压器,2011(8)：57-60.
[90]余刃,张永刚,叶鲁卿ICMMS中的预知维护及其功能层通用参考模型[J].水电能源科学,1999(4)：19-22.
[91]余刃,叶鲁卿,张永刚.智能控制-维护-技术管理集成系统(ICMMS)及其在电力系统中的应用(一)ICMMS的思想、组成及其特征[J].电力系统自动化,1999(23)：50-54.
[92]余刃,叶鲁卿,张永刚.智能控制-维护-技术管理集成系统(ICMMS)及其在电力系统中的应用——(二)ICMMS分析与设计的基本方法[J].电力系统自动化,1999(24)：39-42.
[93]余刃,叶鲁卿,李朝晖.智能控-维护-技术管理集成系统(ICMMS)及其在电力系统中的应用(四)ICMMS框架下水电厂维护子系统的分析与设计方法[J].电力系统自动化,2000(3)：33-36.
[94]Zhaohui L, Youzhong A, Huanxuan S. Optimal Maintenance Information System of Gezhouba Hydro Power Plant[C].In Proceeding of 2007 IEEE Power Engineering Society General Meeting.USA,2007
[95]杨合民,李朝晖,王宏.基于局部放电监测的水轮发电机主绝缘诊断分析系统[J].电力系统自动化,2004(15)：61-66.
[96]万元,李朝晖,薛松,等.水轮发电机局部放电在线监测中的脉冲识别方法[J].高电压技术,2009(9)：2169-2175.
[97]王杰.电力变压器局放超高频监测数据分析[硕士学位论文].武汉：华中科技大学,2010.
[98]刘明军,韩迪,李朝晖HOMS集成环境下变压器局放超高频在线监测[J].高电压技术,2010(8)：1975-1980.
[99]Yuan W, Zhaohui L, Mingjun L, et al. Automatic suppression of white noise and repetitive pulses interference in on-site partial discharge monitoring[C].In Proceedings of IEEE ICEMS. Wuhan,2008:809-813
[100]Liu M, Bi R, Zhou C, et al. Online UHF PD monitoring for transformers:Pulses knowledge acquisition[C].In IEEE PES General Meeting, PES 2010. Minneapolis, MN, United states,2010
[101]刘明军.变压器局放超高频监测与基于知识的分析方法研究[博士学位论文].华中科技大学,2011.
[102]万元.大型发电机局部放电在线监测与分析方法研究[博士学位论文].华中科技大学,2009.
[103]史会轩,李朝晖,毕亚雄.基于声波探测的水轮机空化分析方法研究与应用[J].水利水电技术,2008(9)：75-77.
[104]Shi H, Li Z, Bi Y. An on-line cavitation monitoring system for large kaplan turbines[C].In 2007 IEEE Power Engineering Society General Meeting, PES. Tampa, FL, United states,2007
[105]李初辉.水电机组集成监测系统中稳定性单元研究与开发[硕士学位论文].武汉：华中科技大学,2009.
[106]郭江,李朝晖,陈燚涛.水轮发电机组及其操作可视化仿真系统(英文)[J].中国电机工程学报,2005(7)：137-143.
[107]孔飞,李朝晖.水电机组状态分析信号处理平台的设计与应用[J].水电自动化与大坝监测,2007(5)：37-40.
[108]陈燚涛,李朝晖.基于数字化模型的水轮机调速系统状态监测与分析[J].电力系统自动化,2005(9)：72-76.
[109]张杰.水电机组集成监测系统研究与开发[硕士学位论文].武汉：华中科技大学,2009.
[110]罗云,李朝晖.面向维护的水力发电设备远程实时监视方法[J].水电自动化与大坝监测,2007(1)：57-60.
[111]钟凯.基于.NET的水电厂最优维护Web服务器开发[硕士学位论文].武汉：华中科技大学,2007.
[112]罗云.水电厂远程监测及分析系统研究与开发[硕士学位论文].武汉：华中科技大学,2006.
[113]Zhaohui L, Xingbin Y, Shaoqing N, et al. Maintenance-oriented information digitalization of hydro turbine generator sets[C].In Proc. of 2009 IEEE PES General Meeting. Canada,2009:1-7
[114]陈燚涛,李朝晖.水电厂最优维护信息系统的数据层次性组织策略[J].大电机技术,2007(4)：37-41.
[115]李朝晖,杨贤,毕亚雄.水电机组数字化及其工程应用[J].电力系统自动化,2008(23)：76-80.
[116]杨兴斌,李朝晖.水电机组专家经验和知识的描述方法[J].水电能源科学,2007(1)：46-48.
[117]杨兴斌,李朝晖,艾友忠.基于状态检修的信息集成技术及其应用[J].水力发电,2007(5)：57-59.
[118]胡炎,谢小荣,韩英铎,等.电力信息系统安全体系设计方法综述[J].电网技术,2005(1)：35-39.
[119]胡炎,董名垂,韩英铎.电力工业信息安全的思考[J].电力系统自动化,2002(7)：1-4.
[120]Lakshminarayanan, Sitaraman. An Overview of Web Service Security in ASP.NET[C].In Proceedings of the International Conference on Web Services.2003:250-255
[121]Gordon, A L.2006 CSI/FBI computer crime and security survey[J]. Computer Security Journal, 2006,22(3):1-21.
[122]谢凯,赵转社.湖南电网水调自动化系统安全隔离的设计与实现[J].水电自动化与大坝监测,2008(4)：71-73.
[123]汤文冰.鸭河口电厂电力二次系统网络安全分析[J].电脑与电信,2007(9)：46-48.
[124]陈思勤.华能上海石洞口第二电厂实时系统安全分析及防护对策[J].电网技术,2004(11)：72-75.
[125]高新华,王文,马骁.电力信息网络安全隔离设备的研究[J].电网技术,2003(9)：69-72.
[126]申永辉.电力专用安全隔离装置的原理和应用[J].湖南电力,2006(6)：31-33.
[127]Chen J, Roberts C, Weston P. Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems[J]. Control Engineering Practice,2008,16(5):585-596.
[128]Tang J, Wang Q. Online fault diagnosis and prevention expert system for dredgers[J]. Expert Systems with Applications,2008,34(1):511-521.
[129]Leite D F, Hell M B, Costa Jr. P, et al. Real-time fault diagnosis of nonlinear systems[J]. Nonlinear Analysis:Theory, Methods & Applications,2009,71(12):e2665-e2673.
[130]Niu X, Zhao X. The Study of Fault Diagnosis the High-Voltage Circuit Breaker Based on Neural Network and Expert System[J]. Procedia Engineering,2012,29(2):3286-3291.
[131]Song G, He Y, Chu F, et al. HYDES:A Web-based hydro turbine fault diagnosis system[J]. Expert Systems with Applications,2008,34(1):764-772.
[132]何永勇,任继顺,陈伟,等.水电机组远程状态监测、跟踪分析与故障诊断系统[J].清华大学学报(自然科学版),2006(5)：629-632.
[133]Hui S C, Fong A C M, Jha G. A web-based intelligent fault diagnosis system for customer service support[J]. Engineering Applications of Artificial Intelligence,2001,14(4):537-548.
[134]周叶,潘罗平.基于实时数据库的水电机组远程监测中心设计和实现[J].水电自动化与大坝监测,2011(5)：5-7.
[135]周叶,孙建平.B/S结构水轮机组远程在线监测系统设计与实现[J].湖北电力,2005(6)：27-29.
[136]张玉炳.满足二次防护要求的维护信息网络设计与开发[硕士学位论文].武汉：华中科技大学,2009.
[137]尉学军,刘跃.基于Web的B/S结构实时监控系统[J].贵州工业大学学报(自然科学版),2002(5)：62-63.
[138]陈波.分布式远程故障诊断专家系统的框架及若干关键技术的研究[博士学位论文].大连：大连理工大学,2002.
[139]孔莲芳,何志伟.基于B/S模式设备远程监测诊断系统的研究[J].制造业自动化,2003(11)：41-44.
[140]贾寅波.基于RIA的路面状况智能监测系统[硕士学位论文].武汉：武汉理工大学,2009.
[141]田蕊,谭励,苏维均,等.基于RIA的桥梁结构健康监测状态评估系统[J].计算机工程与设计,2011(8)：2889-2892.
[142]武波.专家系统[M].北京：北京理工大学出版社,2001.
[143]蔡自兴,约翰德尔金,龚涛.高级专家系统：原理、设计及应用[M].北京：科学出版社,2005.
[144]Guo J, Li Z, Chen Y. Visualization of a hydro-electric generating unit and its applications[C].In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Washington, DC, United states,2003:2354-2359
[145]王宏.水电站维护自动化研究与实践[博士学位论文].武汉：华中科技大学,2009.
[146]刘秋华.水轮机调速系统实时在线监测与分析研究[硕士学位论文].华中科技大学,2007.
[2]倪敬敏,何光宇,沈沉,等.美国智能电网评估综述[J].电力系统自动化,2010(8)：9-13.
[3]潘家才,纪浩.智能水电站建设思路[J].水电自动化与大坝监测,2012(1)：1-4.
[4]王益民.坚强智能电网技术标准体系研究框架[J].电力系统自动化,2010(22)：1-6.
[5]冯汉夫,石爽,马琴,等.智能化水电站建设的思考[J].水电厂自动化,2010(4)：1-5.
[6]刘观标,李晓斌,李永红,等.智能水电厂的体系结构[J].水电自动化与大坝监测,2011(1)：1-4.
[7]王海.水轮发电机组状态检修技术[M].武汉：中国电力出版社,2004.
[8]黄树红,李建兰.发电设备状态检修与诊断方法[M].北京：中国电力出版社,2008.
[9]杨兴斌.水电厂技术资料与专家知识数字化方法研究与应用[博士学位论文].武汉：华中科技大学,2009.
[10]虞和济.故障诊断的基本原理[M].北京：冶金工业出版社,1989.
[11]魏晓宾,马小平,李亚朋.故障诊断技术综述[J].煤矿机电,2009(1)：63-65.
[12]彭文季.水电机组振动故障的智能诊断方法研究[博士学位论文].西安：西安理工大学,2007.
[13]张栓柱.小波分析在异步电机故障诊断中的应用研究[J].电机技术,2011(6)：37-40.
[14]裴新才,许同乐.小波分析在轴承故障特征信号降噪中的应用[J].山东理工大学学报(自然科学版),2011(2)：89-91.
[15]Shi J Z, Gu F, Goulding P, et al. Integration of multiple platforms for real-time remote model-based condition monitoring[J]. Computers in Industry,2007,58(6):531-538.
[16]Simani S, Fantuzzi C. Dynamic system identification and model-based fault diagnosis of an industrial gas turbine prototype[J]. Mechatronics,2006,16(6):341-363.
[17]Isermann R. Model-based fault-detection and diagnosis-status and applications [J]. Annual Reviews in Control,2005,29(1):71-85.
[18]Cunningham P. A case study on the use of model-based systems for electronic fault diagnosis[J]. Artificial Intelligence in Engineering,1998,12(3):283-295.
[19]金鑫,任献彬,周亮.智能故障诊断技术研究综述[J].国外电子测量技术,2009(7)：30-32.
[20]陈启卷,张军仿,张超.基于故障树的抽水蓄能电站球阀故障诊断研究[J].水力发电,2010(5)：50-52.
[21]洪冶,蔡维由,乐振春.模糊故障树诊断及应用[J].武汉大学学报(工学版),2001(1)：93-94.
[22]赵新泽,杨明松,彭巍,等.基于故障树分析的闸门双吊点液压启闭系统故障诊断研究[J].水力发电,2011(8)：68-70.
[23]Chang S, Lin C, Chang C. A fuzzy diagnosis approach using dynamic fault trees[J]. Chemical Engineering Science,2002,57(15):2971-2985.
[24]Duan R, Zhou H. A New Fault Diagnosis Method Based on Fault Tree and Bayesian Networks[J]. Energy Procedia,2012,17, Part B(1):1376-1382.
[25]彭文季,罗兴錡.基于小波神经网络的水电机组振动故障诊断研究[J].水力发电学报,2007(1)：123-128.
[26]杨淑红.基于小波神经网络火电厂锅炉故障诊断研究[J].煤炭技术,2011(10)：46-48.
[27]Jia Y, K. T C, Xiangsheng W, et al. Modelling of chiller performance using artificial neural networks[J]. Energy Procedia,2011,13(3):1011-1016.
[28]Saxena A, Saad A. Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems[J]. Applied Soft Computing,2007,7(1):441-454.
[29]Rajakarunakaran S, Venkumar P, Devaraj D, et al. Artificial neural network approach for fault detection in rotary system[J]. Applied Soft Computing,2008,8(1):740-748.
[30]符向前,蒋劲,孙慕群,等.水电机组故障诊断系统中的模糊诊断技术研究[J].华中科技大学学报(自然科学版),2006(1)：81-83.
[31]杨志荣,周建中.基于模糊理论和多智能体的水电机组在线故障诊断研究[J].水力发电,2010(3)：72-74.
[32]陈铁华,陈启卷.模糊聚类分析在水电机组振动故障诊断中的应用[J].中国电机工程学报,2002(3)：44-48.
[33]Sakthivel N R, Sugumaran V, Nair B B. Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump[J]. Mechanical Systems and Signal Processing,2010,24(6):1887-1906.
[34]Han Y, Li Y, Chen Q, et al. A Cooperative Spectrum Sensing Algorithm Based on Fuzzy set and D-S Theory[J]. Energy Procedia,2011,13(5):8376-8382.
[35]Ye J. Fault diagnosis of turbine based on fuzzy cross entropy of vague sets[J]. Expert Systems with Applications,2009,36(4):8103-8106.
[36]张双全,袁晓辉.水电机组在线监测技术与故障诊断专家系统[J].水力发电,2003(7)：43-45.
[37]陈卫钢,周建中,常黎.基于专家系统的水电机组振动故障诊断研究[J].华中科技大学学报(自然科学版),2002(6)：102-104.
[38]Wu J, Bai M R, Su F, et al. An expert system for the diagnosis of faults in rotating machinery using adaptive order-tracking algorithm[J]. Expert Systems with Applications,2009,36(3, Part 1):5424-5431.
[39]Qian Y, Xu L, Li X, et al. LUBRES:An expert system development and implementation for real-time fault diagnosis of a lubricating oil refining process[J]. Expert Systems with Applications,2008,35(3):1252-1266.
[40]Zhi-Ling Y, Bin W, Xing-Hui D, et al. Expert System of Fault Diagnosis for Gear Box in Wind Turbine[J]. Systems Engineering Procedia,2012,4(3):189-195.
[41]Tang J, Wang Q. Online fault diagnosis and prevention expert system for dredgers[J]. Expert Systems with Applications,2008,34(1):511-521.
[42]Wu J, Liu C. An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network[J]. Expert Systems with Applications,2009,36(3, Part 1): 4278-4286.
[43]Li J, Shen S. Research on the Algorithm of Avionic Device Fault Diagnosis Based on Fuzzy Expert System[J]. Chinese Journal of Aeronautics,2007,20(3):223-229.
[44]Yang B, Lim D, Tan A C C. VIBEX:an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table[J]. Expert Systems with Applications,2005, 28(4):735-742.
[45]吴炜,陈喜阳.基于小波——神经网络的水电机组远程监测诊断系统[J].湖北电力,2005(4)：3-4.
[46]梁武科,赵道利,马薇,等.基于粗糙集-RBF神经网络的水电机组故障诊断[J].仪器仪表学报,2007(10)：1806-1810.
[47]张孝远,周建中,黄志伟,等.基于粗糙集和多类支持向量机的水电机组振动故障诊断[J].中国电机工程学报,2010(20)：88-93.
[48]安学利,周建中,刘力,等.基于熵权理论和信息融合技术的水电机组振动故障诊断[J].电力系统自动化,2008(20)：78-82.
[49]彭兵,周建中,方仍存,等.基于开机过程信息融合的水电机组故障诊断方法[J].电力系统自动化,2008(13)：76-80.
[50]赵道利,马薇,梁武科,等.水电机组振动故障的信息融合诊断与仿真研究[J].中国电机工程学报,2005(20)：137-142.
[51]彭文季,郭鹏程,罗兴錡.基于最小二乘支持向量机和信息融合技术的水电机组振动故障 诊断研究[J].水力发电学报,2007(6)：137-142.
[52]彭兵.基于改进支持向量机和特征信息融合的水电机组故障诊断[博士学位论文].武汉：华中科技大学,2008.
[53]李郁侠,刘立峰,陈继尧,等.基于神经网络和证据理论融合的水电机组振动故障诊断研究[J].西北农林科技大学学报(自然科学版),2005(10)：115-119.
[54]刘立峰,李郁侠,王伟.基于遗传神经网络和证据理论融合的水电机组振动故障诊断研究[J].水力发电学报,2008(5)：163-167.
[55]梁武科,赵道利,王荣荣,等.水电机组振动故障的粗糙集-神经网络诊断方法[J].西北农林科技大学学报(自然科学版),2007(7)：223-226.
[56]梁武科,赵道利,马薇,等.基于粗糙集-RBF神经网络的水电机组故障诊断[J].仪器仪表学报,2007(10)：1806-1810.
[57]Wu J, Kuo J. An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network[J]. Expert Systems with Applications,2009,36(6):9776-9783.
[58]Saravanan N, Siddabattuni V N S K, Ramachandran K I. Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM)[J]. Applied Soft Computing,2010,10(1):344-360.
[59]余文宁.大型水轮发电机组状态监测与智能故障诊断系统研究[硕士学位论文].长沙：中南大学,2007.
[60]于晶.DCS-分散控制系统及应用现状[J].工业仪表与自动化装置,1993(4)：43-47.
[61]宋天相,王义.恩泰克预测维修系统在船舶诊断分析中的应用[C].见：95全国设备故障诊断技术学术论文集.武汉,1995：1014-1018
[62]严可国,魏克严,李植.大型旋转机械监测保护故障诊断系统[M].北京：美国本特利内华达公司北京办事处,2002.
[63]S B J, G P A. Plant vibration monitoring at loviisa nuclear power plant[J]. Noise and vibration, 1991,22(8):34-37.
[64]Benati, C F, C S G. Vibration monitoring and mechanical diagnosis on large rotating machinery in ENEL power plants [J]. Symposium on Diagnostics Rotating Machines in Power Plants, 1993(10):27-29.
[65]Tanaka M. The diagnosis technologies in power plant in Japan[J]. Symposium on Diagnostics Rotating Machines in Power Plants,1993:27-29.
[66]王正旭,邵先荣.克拉斯诺雅尔斯克水电站设备的诊断[J].水利水电快报,2003,24(19)：5-8.
[67]李辉.基于粗糙集的水轮发电机组振动故障诊断系统研究[硕士学位论文].西安：西安理工大学,2006.
[68]刘峰.基于神经网络的水轮发电机组振动故障诊断专家系统的研究[硕士学位论文].西安：西安理工大学,2003.
[69]张彦宁.基于小波分析的水电机组在线监测与故障诊断的研究[硕士学位论文].西安：西安理工大学,2003.
[70]余涛,王晶,高峰,等.水电机组故障诊断专家系统研究现状与发展前景[J].云南电力技术,1999(2)：52-55.
[71]朱燕.水轮机组状态监测及故障分析系统研究开发[硕士学位论文].南京：东南大学,2007.
[72]王宇珩,徐立新,戴亚平,等.水电机组旋转机械故障诊断专家系统研究[J].新技术新工艺,2003(4)：7-9.
[73]褚福磊,卢文秀,张伟,等.水泵水轮机组状态监测与故障诊断系统[J].水力发电,1999(2)：31-33.
[74]沈文辉,陈国栋.水电厂机组状态监测系统与计算机监控系统的数据共享[J].水电自动化与大坝监测,2004(5)：7-10.
[75]任乐鸣,李文清.机组状态监测与故障诊断系统在紧水滩水电厂的应用[J].水电能源科学, 2006(2)：94-96.
[76]张云峰.密云水电厂监控系统技术改造[J].水电厂自动化,2005(1)：181-184.
[77]全新建,朱辰,施冲,等.基于三峡电站监控系统的状态监测趋势分析系统[J].水电厂自动化,2005(1)：138-142.
[78]曹锋.水轮发电机组诊断方法研究及信息平台设计[硕士学位论文].武汉：华中科技大学,2003.
[79]艾友忠.葛洲坝电厂最优维护方法研究与实践[博士学位论文].武汉：华中科技大学,2007.
[80]董毓新.水轮发电机组振动[M].辽宁：大连理工大学出版社,1989.
[81]马震岳,董毓新.水轮发电机组动力学[M].辽宁：大连理工出版社,2004.
[82]史会轩.大型水轮机空化在线监测与分析方法及应用研究[博士学位论文].武汉：华中科技大学,2008.
[83]陈燚涛.水轮机调速系统优化维护理论与实践[博士学位论文].华中科技大学,2005.
[84]何建明.智能电网信息和通信技术关键问题探讨[J].企业技术开发,2012(1)：32-34.
[85]高勤.NARI-NC2000计算机监控系统在葛洲坝二江电厂的应用[J].水电厂自动化,2008(4)：15-19.
[86]张家治.NC2000计算机监控系统在葛洲坝电厂的应用[J].水电厂自动化,2008(3)：20-21.
[87]易发德.葛洲坝大江厂计算机监控系统[J].华中电力,1994(6)：49-55.
[88]郭江,林霖,曹禹,等.基于Web的水电站大坝巡检系统设计与开发[J].水电能源科学,2010(7)：54-56.
[89]韩长利,仇明,李智.用油色谱分析方法检测变压器故障[J].变压器,2011(8)：57-60.
[90]余刃,张永刚,叶鲁卿ICMMS中的预知维护及其功能层通用参考模型[J].水电能源科学,1999(4)：19-22.
[91]余刃,叶鲁卿,张永刚.智能控制-维护-技术管理集成系统(ICMMS)及其在电力系统中的应用(一)ICMMS的思想、组成及其特征[J].电力系统自动化,1999(23)：50-54.
[92]余刃,叶鲁卿,张永刚.智能控制-维护-技术管理集成系统(ICMMS)及其在电力系统中的应用——(二)ICMMS分析与设计的基本方法[J].电力系统自动化,1999(24)：39-42.
[93]余刃,叶鲁卿,李朝晖.智能控-维护-技术管理集成系统(ICMMS)及其在电力系统中的应用(四)ICMMS框架下水电厂维护子系统的分析与设计方法[J].电力系统自动化,2000(3)：33-36.
[94]Zhaohui L, Youzhong A, Huanxuan S. Optimal Maintenance Information System of Gezhouba Hydro Power Plant[C].In Proceeding of 2007 IEEE Power Engineering Society General Meeting.USA,2007
[95]杨合民,李朝晖,王宏.基于局部放电监测的水轮发电机主绝缘诊断分析系统[J].电力系统自动化,2004(15)：61-66.
[96]万元,李朝晖,薛松,等.水轮发电机局部放电在线监测中的脉冲识别方法[J].高电压技术,2009(9)：2169-2175.
[97]王杰.电力变压器局放超高频监测数据分析[硕士学位论文].武汉：华中科技大学,2010.
[98]刘明军,韩迪,李朝晖HOMS集成环境下变压器局放超高频在线监测[J].高电压技术,2010(8)：1975-1980.
[99]Yuan W, Zhaohui L, Mingjun L, et al. Automatic suppression of white noise and repetitive pulses interference in on-site partial discharge monitoring[C].In Proceedings of IEEE ICEMS. Wuhan,2008:809-813
[100]Liu M, Bi R, Zhou C, et al. Online UHF PD monitoring for transformers:Pulses knowledge acquisition[C].In IEEE PES General Meeting, PES 2010. Minneapolis, MN, United states,2010
[101]刘明军.变压器局放超高频监测与基于知识的分析方法研究[博士学位论文].华中科技大学,2011.
[102]万元.大型发电机局部放电在线监测与分析方法研究[博士学位论文].华中科技大学,2009.
[103]史会轩,李朝晖,毕亚雄.基于声波探测的水轮机空化分析方法研究与应用[J].水利水电技术,2008(9)：75-77.
[104]Shi H, Li Z, Bi Y. An on-line cavitation monitoring system for large kaplan turbines[C].In 2007 IEEE Power Engineering Society General Meeting, PES. Tampa, FL, United states,2007
[105]李初辉.水电机组集成监测系统中稳定性单元研究与开发[硕士学位论文].武汉：华中科技大学,2009.
[106]郭江,李朝晖,陈燚涛.水轮发电机组及其操作可视化仿真系统(英文)[J].中国电机工程学报,2005(7)：137-143.
[107]孔飞,李朝晖.水电机组状态分析信号处理平台的设计与应用[J].水电自动化与大坝监测,2007(5)：37-40.
[108]陈燚涛,李朝晖.基于数字化模型的水轮机调速系统状态监测与分析[J].电力系统自动化,2005(9)：72-76.
[109]张杰.水电机组集成监测系统研究与开发[硕士学位论文].武汉：华中科技大学,2009.
[110]罗云,李朝晖.面向维护的水力发电设备远程实时监视方法[J].水电自动化与大坝监测,2007(1)：57-60.
[111]钟凯.基于.NET的水电厂最优维护Web服务器开发[硕士学位论文].武汉：华中科技大学,2007.
[112]罗云.水电厂远程监测及分析系统研究与开发[硕士学位论文].武汉：华中科技大学,2006.
[113]Zhaohui L, Xingbin Y, Shaoqing N, et al. Maintenance-oriented information digitalization of hydro turbine generator sets[C].In Proc. of 2009 IEEE PES General Meeting. Canada,2009:1-7
[114]陈燚涛,李朝晖.水电厂最优维护信息系统的数据层次性组织策略[J].大电机技术,2007(4)：37-41.
[115]李朝晖,杨贤,毕亚雄.水电机组数字化及其工程应用[J].电力系统自动化,2008(23)：76-80.
[116]杨兴斌,李朝晖.水电机组专家经验和知识的描述方法[J].水电能源科学,2007(1)：46-48.
[117]杨兴斌,李朝晖,艾友忠.基于状态检修的信息集成技术及其应用[J].水力发电,2007(5)：57-59.
[118]胡炎,谢小荣,韩英铎,等.电力信息系统安全体系设计方法综述[J].电网技术,2005(1)：35-39.
[119]胡炎,董名垂,韩英铎.电力工业信息安全的思考[J].电力系统自动化,2002(7)：1-4.
[120]Lakshminarayanan, Sitaraman. An Overview of Web Service Security in ASP.NET[C].In Proceedings of the International Conference on Web Services.2003:250-255
[121]Gordon, A L.2006 CSI/FBI computer crime and security survey[J]. Computer Security Journal, 2006,22(3):1-21.
[122]谢凯,赵转社.湖南电网水调自动化系统安全隔离的设计与实现[J].水电自动化与大坝监测,2008(4)：71-73.
[123]汤文冰.鸭河口电厂电力二次系统网络安全分析[J].电脑与电信,2007(9)：46-48.
[124]陈思勤.华能上海石洞口第二电厂实时系统安全分析及防护对策[J].电网技术,2004(11)：72-75.
[125]高新华,王文,马骁.电力信息网络安全隔离设备的研究[J].电网技术,2003(9)：69-72.
[126]申永辉.电力专用安全隔离装置的原理和应用[J].湖南电力,2006(6)：31-33.
[127]Chen J, Roberts C, Weston P. Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems[J]. Control Engineering Practice,2008,16(5):585-596.
[128]Tang J, Wang Q. Online fault diagnosis and prevention expert system for dredgers[J]. Expert Systems with Applications,2008,34(1):511-521.
[129]Leite D F, Hell M B, Costa Jr. P, et al. Real-time fault diagnosis of nonlinear systems[J]. Nonlinear Analysis:Theory, Methods & Applications,2009,71(12):e2665-e2673.
[130]Niu X, Zhao X. The Study of Fault Diagnosis the High-Voltage Circuit Breaker Based on Neural Network and Expert System[J]. Procedia Engineering,2012,29(2):3286-3291.
[131]Song G, He Y, Chu F, et al. HYDES:A Web-based hydro turbine fault diagnosis system[J]. Expert Systems with Applications,2008,34(1):764-772.
[132]何永勇,任继顺,陈伟,等.水电机组远程状态监测、跟踪分析与故障诊断系统[J].清华大学学报(自然科学版),2006(5)：629-632.
[133]Hui S C, Fong A C M, Jha G. A web-based intelligent fault diagnosis system for customer service support[J]. Engineering Applications of Artificial Intelligence,2001,14(4):537-548.
[134]周叶,潘罗平.基于实时数据库的水电机组远程监测中心设计和实现[J].水电自动化与大坝监测,2011(5)：5-7.
[135]周叶,孙建平.B/S结构水轮机组远程在线监测系统设计与实现[J].湖北电力,2005(6)：27-29.
[136]张玉炳.满足二次防护要求的维护信息网络设计与开发[硕士学位论文].武汉：华中科技大学,2009.
[137]尉学军,刘跃.基于Web的B/S结构实时监控系统[J].贵州工业大学学报(自然科学版),2002(5)：62-63.
[138]陈波.分布式远程故障诊断专家系统的框架及若干关键技术的研究[博士学位论文].大连：大连理工大学,2002.
[139]孔莲芳,何志伟.基于B/S模式设备远程监测诊断系统的研究[J].制造业自动化,2003(11)：41-44.
[140]贾寅波.基于RIA的路面状况智能监测系统[硕士学位论文].武汉：武汉理工大学,2009.
[141]田蕊,谭励,苏维均,等.基于RIA的桥梁结构健康监测状态评估系统[J].计算机工程与设计,2011(8)：2889-2892.
[142]武波.专家系统[M].北京：北京理工大学出版社,2001.
[143]蔡自兴,约翰德尔金,龚涛.高级专家系统：原理、设计及应用[M].北京：科学出版社,2005.
[144]Guo J, Li Z, Chen Y. Visualization of a hydro-electric generating unit and its applications[C].In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Washington, DC, United states,2003:2354-2359
[145]王宏.水电站维护自动化研究与实践[博士学位论文].武汉：华中科技大学,2009.
[146]刘秋华.水轮机调速系统实时在线监测与分析研究[硕士学位论文].华中科技大学,2007.