水电站运行管理支持系统
摘要
在水电站的建设与运行过程中,技术人员经常需要查阅技术资料和技术图纸,一张张地去翻阅资料和图纸,既浪费时间又容易丢失。水电站运行管理支持系统用于水电站建设、运行过程中图文档管理和水电厂站内水力机组优化运行。主要包括:图档管理系统;文档管理系统;优化运行决策支持系统。
第一部分:水电站建设、运行过程中的图档管理、文档管理。图档、文档管理系统采用了J2EE的Web构件如JSP、Servlet、JDBC、JSTL等和以SQL Server 2000为数据库管理系统,选择MVC(模型—视图—控制器)模式作为系统的设计模式,能够通过普通的IE浏览器来管理图文档数据库,实现对电子图档的远程或异地管理,支持大数据量的图纸资料信息管理,数据安全性和完整性可以得到保证。
第二部分:优化运行决策支持系统。采用人工神经网络方法对各水力机组的综合运转特性进行拟合,得到各机组的效率特性人工神经网络模型。利用神经网络的非线性映射特性和遗传算法,将神经网络与遗传算法进行有机结合,实现机组优化组合的计算。在系统中实现了水电站的机组耗流量模型、建立优化以及优化结果处理三个功能,以便运行人员直观的了解水轮机的运行状况。从而对水轮机机组的运行情况进行调节,达到在给定总流量的情况下,以实现最大出力的经济运行目的。
本系统的开发对于水电站的日常管理,充分利用水资源,提高水电站的经济效益,实现水力机组的高效稳定运行以及优化运行具有一定实际意义。
In the process of hydropower station construction and operation, the technical staff often need to look up technical information and technical drawings one by one, which is been a waste of time and can easily lost. Hydropower station operation management support system is mainly used in the construction of hydropower stations and document management and hydropower station reservoir optimization, which including picture management system, document management system and optimal operation of decision support systems.
The first part: Files management and documents management during the construction and movement process of hydroelectric power station. The files and documents management system use Web technologies of J2EE--like JSP, Servlet, JDBC, JSTL etc. It takes SQL Server 2000 as the database management system and chose MVC (model - view - controller) pattern as system's design pattern. Ordinary IE browser could be used to manage the chart documents database and implement long-distance or different place management. It also supports the management of great data quantity information, of which the security and integrity of data may guarantee.
The second part: optimal operation of decision support systems. Artificial neural network is integrated the various operating characteristics of hydraulic units which were fitted to obtain the efficiency characteristics of each unit of artificial neural network model. Nonlinear mapping using neural networks and genetic characteristics of parallel algorithms, random, adaptive search feature, the neural network is combined with genetic algorithm to achieve optimal combination of the computation unit. In the system the station's unit consumption of traffic model, optimization, and optimization results can be displayed which the staff could run the turbine operating conditions intuitively. Thus in a given situation, the operation of the turbine unit can be adjusted to achieve the economic operation maximum output.
This system's development has certainly practical significance in the common management, using the water resources fully, enhancing the economic efficiency, realising highly effective movement of power set as well as the optimized movement regarding the hydroelectric power station.
第一部分:水电站建设、运行过程中的图档管理、文档管理。图档、文档管理系统采用了J2EE的Web构件如JSP、Servlet、JDBC、JSTL等和以SQL Server 2000为数据库管理系统,选择MVC(模型—视图—控制器)模式作为系统的设计模式,能够通过普通的IE浏览器来管理图文档数据库,实现对电子图档的远程或异地管理,支持大数据量的图纸资料信息管理,数据安全性和完整性可以得到保证。
第二部分:优化运行决策支持系统。采用人工神经网络方法对各水力机组的综合运转特性进行拟合,得到各机组的效率特性人工神经网络模型。利用神经网络的非线性映射特性和遗传算法,将神经网络与遗传算法进行有机结合,实现机组优化组合的计算。在系统中实现了水电站的机组耗流量模型、建立优化以及优化结果处理三个功能,以便运行人员直观的了解水轮机的运行状况。从而对水轮机机组的运行情况进行调节,达到在给定总流量的情况下,以实现最大出力的经济运行目的。
本系统的开发对于水电站的日常管理,充分利用水资源,提高水电站的经济效益,实现水力机组的高效稳定运行以及优化运行具有一定实际意义。
In the process of hydropower station construction and operation, the technical staff often need to look up technical information and technical drawings one by one, which is been a waste of time and can easily lost. Hydropower station operation management support system is mainly used in the construction of hydropower stations and document management and hydropower station reservoir optimization, which including picture management system, document management system and optimal operation of decision support systems.
The first part: Files management and documents management during the construction and movement process of hydroelectric power station. The files and documents management system use Web technologies of J2EE--like JSP, Servlet, JDBC, JSTL etc. It takes SQL Server 2000 as the database management system and chose MVC (model - view - controller) pattern as system's design pattern. Ordinary IE browser could be used to manage the chart documents database and implement long-distance or different place management. It also supports the management of great data quantity information, of which the security and integrity of data may guarantee.
The second part: optimal operation of decision support systems. Artificial neural network is integrated the various operating characteristics of hydraulic units which were fitted to obtain the efficiency characteristics of each unit of artificial neural network model. Nonlinear mapping using neural networks and genetic characteristics of parallel algorithms, random, adaptive search feature, the neural network is combined with genetic algorithm to achieve optimal combination of the computation unit. In the system the station's unit consumption of traffic model, optimization, and optimization results can be displayed which the staff could run the turbine operating conditions intuitively. Thus in a given situation, the operation of the turbine unit can be adjusted to achieve the economic operation maximum output.
This system's development has certainly practical significance in the common management, using the water resources fully, enhancing the economic efficiency, realising highly effective movement of power set as well as the optimized movement regarding the hydroelectric power station.
引文
[1]徐晨光,刘晓黎,黄强等.水电站经济运行理论及算法实现[M].黄河水利出版社,2006,1
[2]李清光,王华昌,李志刚等.基于Web的PDM系统开发技术研究[J].计算机辅助工程,2002, (9):20~23
[3] Benoit Eynard,Thomas Gallet,Lionel Roucoules. PDM system implementation based on UML. Mathematics and Coputers in Simulation,2006,70 (5):330~342
[4] Gastro F,Pescina M,Llort G.Reliability improvements of the Guri Hydroelectric Power P;ant control systerm AGC and AVC.IEEE Transactions on Energy Converdion,1992,7 (3):447~452
[5] PaerkJ H.Economical load dispatch for piecewise quadratic cost function using Hopfeild Neural Network.IEEE Trans on Power Systerm,1993,8 (3)
[6]韦柳涛,曾庆川,姜铁兵.启发式遗传基因算法及其在电力系统机组组合优化中的应用[J].中国电机工程学报,1994,14 (2):67~72
[7]杨侃.大型水电站经济运行的多重动态规划模型[J].河海大学学报,1995,23 (4):85~90
[8]梅亚东,左园忠,朱教新.一类含有0-1变量的厂内经济运行模型及解法[J].水电能源科学,1999,17 (3):27~30
[9]王德意,南海鹏,刘栋等.快速动态规划法在运行机组间有功负荷分配中的应用[J].水利水电技术,2001,32 (6):28~30
[10]刘建国,赵林明.基于计划规划原理的水电厂经济运行算法[J].水力发电学报,2000,(1):22~26
[11]钟德源,林国,程志艳. JSP实用简明教程[M].清华大学出版社,2009,(3):59~60
[12]赵东等.基于WEB三层结构在制动供应链中的应用[J].计算机工程,2002,28 (1):222~225
[13]徐甲同,陆丽娜,谷建华.计算机操作系统教程[M].西安电子科技大学出版社,2001.2
[14]毛汉书.网络技术基础[M].人民邮电出版社,2000.5
[15]龚永生.当前流行的J2EE WEB应用架构分析
[16] Jeffrey A. Hoffer, Mary B. prescott, Fred R. MeFadden. Modern Database Managemen (7thEdition)[M], Prentice Hall, 2004:22~23
[17]沈磊.采用标准耗水率指标推进水电厂经济运行[J],水力发电,1997,(11):45~46
[18]袁晓辉,袁艳斌,王金文等.水火电力系统短期发电计划优化方法综述[J].中国电力,2002 (9):33~38
[19]博宏涛.综合经济目标下的电厂汽轮机组最优负荷分配[D].上海交通人学图书馆,2001
[20]张勇传.水电站经济运行原理(第二版)[M].中国水利水电出版社,1998
[21]叶秉如.水资源系统优化规划和调度[M].中国水利水电出版社,2001
[22]朱大奇.人工神经网络研究现状及其展望[J].江南大学学报(自然科学版),2004,(1):103~107
[23]王伟.人工神经网络原理与应用[M].北京:北京航空航天大学出版社,1995
[24]党建武.神经网络技术及应用[M].北京:中国铁道出版社,2000
[25]胡守仁等.神经网络应用技术[M].北京:国防科技大学出版社,1993
[26]孔亮,王燕昌,郑颖人.人工神经网络及其在岩土建模中的应用[J].宁夏大学学报,2000, 21 (3):214~216
[27]陈浩勇,王锡凡.机组组合问题的优化方法综述[J].电力系统自动化.1999, 23 (4):51~56
[28] D.E.Goldberg. Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley,1989
[29]陆金桂,李谦,王浩等.遗传算法原理及其工程应用[J].中国矿业大学出版社,1997
[30]祁兴会.智能遗传算法在灌区水资源多目标优化调度中的应用与研究[D].郑州:华北水利水电学院,2004
[31]聂相田,赵国滚.考虑计算时段间径流变化过程的随机动态规划模型[J].华北水利水电学院学报,1996,17 (2):13~18
[32]陈国良,王煦法,庄镇泉等.遗传算法及其应用[M].北京:人民邮电出版社,1996,6
[33]周明,孙树栋著.遗传算法原理及应用[M].北京:国防工业出版社,1999:33~45
[34]冯迅,王金文,权先璋等.遗传算法在水电站优化调度中的实用研究[J].华中电力,2005,18 (3):12~15
[35] J.H.Holland. Adaptation in Natural and Artificial Systems.University of Michigan Press, Ann
[36]刘国东,丁晶.BP网络用于水文预测的几个问题探讨[J].水利学报,1999,(1):65 ~69.
[37]丁晶,邓育仁,安雪松.人工神经前馈BP网络模型用作过渡期径流预测[J].水电站设计,1997 (2):69 ~74
[38]邱林,陈守煜,聂相田.模糊模式识别神经网络预测模型及其应用[J].水科学进展,1908,9 (3):258~264
[39]吕武.中小企业技术图档管理研究[J].制造业信息化,2003,12
[40]陈吉光,宋宝谧,徐振越.工程图档管理软件的设计开发[J].大连铁道学院学报,2004,6
[41]邢海东,孟志勇.图文档管理系统[J].工程设计CAD与智能建筑,2001,5
[42]童秉抠,徐晓慧.产品数据管理(PDM)实施中的存在问题与对策[J].工程图学学报,2002,23 (2)
[43]耿剑峰.基于J2EE平台的Web应用研究与实现[D].江苏南京:河海大学,2004
[44]张勇传.水电站经济运行原理[J].中国水利水电出版社,1998
[45]铁兵,梁年声,康玲,黄定疆.用遗传法确定水电站自动发电计划[J].水利发电学报,1995,4
[46]王黎,马光文.基于遗传算法的水电站厂内经济运行新算法[J].中国电机工程学报,1998,18 (1)
[47]李建中,王珊.数据库系统原理[M].北京:电子工业出版社,1998
[48]陈维默.数据库及其应用[M].中国电力出版社,2005
[49]高传善,钱松荣,毛进林编著.数据通信与计算机网[M].北京:高等教育出版社,2001
[50]冯国章,李佩成.人工神经网络结构对径流预报精度的影响分析[J].资源学报,1998,(2):169~174
[51]张仁贡等.遗传算法在水电站厂内经济运行中的应用[J].华北水利水电学院学报,2006
[2]李清光,王华昌,李志刚等.基于Web的PDM系统开发技术研究[J].计算机辅助工程,2002, (9):20~23
[3] Benoit Eynard,Thomas Gallet,Lionel Roucoules. PDM system implementation based on UML. Mathematics and Coputers in Simulation,2006,70 (5):330~342
[4] Gastro F,Pescina M,Llort G.Reliability improvements of the Guri Hydroelectric Power P;ant control systerm AGC and AVC.IEEE Transactions on Energy Converdion,1992,7 (3):447~452
[5] PaerkJ H.Economical load dispatch for piecewise quadratic cost function using Hopfeild Neural Network.IEEE Trans on Power Systerm,1993,8 (3)
[6]韦柳涛,曾庆川,姜铁兵.启发式遗传基因算法及其在电力系统机组组合优化中的应用[J].中国电机工程学报,1994,14 (2):67~72
[7]杨侃.大型水电站经济运行的多重动态规划模型[J].河海大学学报,1995,23 (4):85~90
[8]梅亚东,左园忠,朱教新.一类含有0-1变量的厂内经济运行模型及解法[J].水电能源科学,1999,17 (3):27~30
[9]王德意,南海鹏,刘栋等.快速动态规划法在运行机组间有功负荷分配中的应用[J].水利水电技术,2001,32 (6):28~30
[10]刘建国,赵林明.基于计划规划原理的水电厂经济运行算法[J].水力发电学报,2000,(1):22~26
[11]钟德源,林国,程志艳. JSP实用简明教程[M].清华大学出版社,2009,(3):59~60
[12]赵东等.基于WEB三层结构在制动供应链中的应用[J].计算机工程,2002,28 (1):222~225
[13]徐甲同,陆丽娜,谷建华.计算机操作系统教程[M].西安电子科技大学出版社,2001.2
[14]毛汉书.网络技术基础[M].人民邮电出版社,2000.5
[15]龚永生.当前流行的J2EE WEB应用架构分析
[16] Jeffrey A. Hoffer, Mary B. prescott, Fred R. MeFadden. Modern Database Managemen (7thEdition)[M], Prentice Hall, 2004:22~23
[17]沈磊.采用标准耗水率指标推进水电厂经济运行[J],水力发电,1997,(11):45~46
[18]袁晓辉,袁艳斌,王金文等.水火电力系统短期发电计划优化方法综述[J].中国电力,2002 (9):33~38
[19]博宏涛.综合经济目标下的电厂汽轮机组最优负荷分配[D].上海交通人学图书馆,2001
[20]张勇传.水电站经济运行原理(第二版)[M].中国水利水电出版社,1998
[21]叶秉如.水资源系统优化规划和调度[M].中国水利水电出版社,2001
[22]朱大奇.人工神经网络研究现状及其展望[J].江南大学学报(自然科学版),2004,(1):103~107
[23]王伟.人工神经网络原理与应用[M].北京:北京航空航天大学出版社,1995
[24]党建武.神经网络技术及应用[M].北京:中国铁道出版社,2000
[25]胡守仁等.神经网络应用技术[M].北京:国防科技大学出版社,1993
[26]孔亮,王燕昌,郑颖人.人工神经网络及其在岩土建模中的应用[J].宁夏大学学报,2000, 21 (3):214~216
[27]陈浩勇,王锡凡.机组组合问题的优化方法综述[J].电力系统自动化.1999, 23 (4):51~56
[28] D.E.Goldberg. Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley,1989
[29]陆金桂,李谦,王浩等.遗传算法原理及其工程应用[J].中国矿业大学出版社,1997
[30]祁兴会.智能遗传算法在灌区水资源多目标优化调度中的应用与研究[D].郑州:华北水利水电学院,2004
[31]聂相田,赵国滚.考虑计算时段间径流变化过程的随机动态规划模型[J].华北水利水电学院学报,1996,17 (2):13~18
[32]陈国良,王煦法,庄镇泉等.遗传算法及其应用[M].北京:人民邮电出版社,1996,6
[33]周明,孙树栋著.遗传算法原理及应用[M].北京:国防工业出版社,1999:33~45
[34]冯迅,王金文,权先璋等.遗传算法在水电站优化调度中的实用研究[J].华中电力,2005,18 (3):12~15
[35] J.H.Holland. Adaptation in Natural and Artificial Systems.University of Michigan Press, Ann
[36]刘国东,丁晶.BP网络用于水文预测的几个问题探讨[J].水利学报,1999,(1):65 ~69.
[37]丁晶,邓育仁,安雪松.人工神经前馈BP网络模型用作过渡期径流预测[J].水电站设计,1997 (2):69 ~74
[38]邱林,陈守煜,聂相田.模糊模式识别神经网络预测模型及其应用[J].水科学进展,1908,9 (3):258~264
[39]吕武.中小企业技术图档管理研究[J].制造业信息化,2003,12
[40]陈吉光,宋宝谧,徐振越.工程图档管理软件的设计开发[J].大连铁道学院学报,2004,6
[41]邢海东,孟志勇.图文档管理系统[J].工程设计CAD与智能建筑,2001,5
[42]童秉抠,徐晓慧.产品数据管理(PDM)实施中的存在问题与对策[J].工程图学学报,2002,23 (2)
[43]耿剑峰.基于J2EE平台的Web应用研究与实现[D].江苏南京:河海大学,2004
[44]张勇传.水电站经济运行原理[J].中国水利水电出版社,1998
[45]铁兵,梁年声,康玲,黄定疆.用遗传法确定水电站自动发电计划[J].水利发电学报,1995,4
[46]王黎,马光文.基于遗传算法的水电站厂内经济运行新算法[J].中国电机工程学报,1998,18 (1)
[47]李建中,王珊.数据库系统原理[M].北京:电子工业出版社,1998
[48]陈维默.数据库及其应用[M].中国电力出版社,2005
[49]高传善,钱松荣,毛进林编著.数据通信与计算机网[M].北京:高等教育出版社,2001
[50]冯国章,李佩成.人工神经网络结构对径流预报精度的影响分析[J].资源学报,1998,(2):169~174
[51]张仁贡等.遗传算法在水电站厂内经济运行中的应用[J].华北水利水电学院学报,2006