基于柔性SCADA的电网复杂故障诊断方法的研究
|
摘要
电网故障诊断课题的定位是依据故障综合信息,借助于知识库或模型,采用某种诊断机制来确定电网故障元件及分析故障原因,同时完成对保护装置、安全自动装置等监测、控制设备的动作行为的评价。目前的故障诊断方法在全局化和实用化中仍存在一些问题,主要原因主要有以下几点,一是信息不全,二是信息噪声干扰大,三是诊断方法在信息不完备的情况下鲁棒性较差。为此,论文对基于柔性SCADA系统的电力系统复杂故障诊断方法进行研究。提出了信息交互性的柔性SCADA系统;在报警信息的处理方面,开发了智能报警系统;在此基础上,本文结合Petri网和专家系统的特点,提出了基于正反向推理的Petri网故障诊断模型,并用WAMS信息对故障信息和诊断结果进行验证。
本文首先对信息完备化做了深入的研究,提出建立基于广域网的柔性SCADA系统,对故障信息按重要程度进行分级上传,同时具有主动上传和等待召唤两种方式,为故障诊断提供灵活、有效的数据交换和功能协调的平台。柔性SCADA系统不再是点对点的单向固定路径的信息交互,而是能实现网络中任意两个(或多个)节点间双向的信息交互,体现子站和主站信息交换的智能性,它是对传统SCADA的扩充和发展,拓宽了系统的数据来源,为故障诊断模块丰富了数据基础。
其次,针对电力系统发生大面积复杂故障后,报警信息存在大量的噪声干扰问题,对报警信息处理和智能报警进行了研究,采用数据挖掘的方法,从历史数据中挖掘出规则,去除实时报警信息的噪声,在此基础上,提出用正向产生式推理,优先和分级报警,减少报警信息数量并提高报警质量和正确率。同时,将去噪后的信息提供给故障诊断模块进行故障分析,提高故障诊断信息的正确率。
最后,本文提出了基于柔性SCADA系统的电力系统复杂故障诊断方法,对全局分层信息的电网故障诊断模型进行了研究。充分利用柔性SCADA的交互性特点,实现了报警信息的分层传输和分层利用,满足了简单故障的快速诊断和复杂故障的准确诊断要求。对于复杂故障,设计了应用元件Petri网故障诊断模型正向推理来确定故障元件方法。当报警信息不完全正确时,提出了应用元件保护配置时延Petri网进行报警信息纠错处理方法,提高容错能力。本文还提出了引用WAMS数据对报警信息纠错处理结果和故障诊断结果进行验证的方法,可进一步提高故障诊断的可靠性和正确性。经算例仿真验证了本文方法的有效性。
The issue of fault diagnosis of power system is a kind of research that based on fault comprehensive information and Knowledge-Based System or model, using kind of diagnosis mechanism to determine fault equipments or reason, at the same time appraising the actions of protecting devices, automatic security devices and other such as automatic monitoring and controlling equipments. At present, the fault diagnosis methods still have some problems in overall situation and in practical. There are three main reasons. First, the information is incomplete. Second, the information noise interference is too large. Third, the robustness of diagnostic is too poor while the information is incomplete. To this end, the paper research about complex fault diagnosis method based on the flexible SCADA in power system. A flexible SCADA system that information can exchange is put forward. In the alarm information processing, intelligent alarm system is developed. After that, Petri nets fault diagnosis model based on forward and backward reasoning is put forward, combining the features Petri nets and expert system. The fault information and diagnostic results is verified by using WAMS information.
First of all, the paper did a deep research to complete the information and raised to create the flexible SCADA system based on wide area network, which can classify and upload according to importance of fault information, having initiative and waiting two patterns at the same time, in order to provide a flexible and effective platform of data exchange and functions coordination for fault diagnosis. The flexible SCADA system is no longer a fixed point-to-point one-way path of information exchange, but the network can achieve any two (or more) nodes of information exchange in two-way, which can reflect intelligence of information exchange between master- station and the sub-stations. It is the development and expansion of traditional SCADA, expanding the system's data sources for the fault diagnosis model.
Secondly, as there are a lot of noise problems of warning messages after large-area complex fault in power system, information processing and intelligent alarm was studied in this paper. Noises of real-time alarm information are removed from historical database using data mining methods. On this basis, forward and backward reasoning is put forward to have priority classification alarm and reduce the number of alarm information and improve the quality and accuracy of alarm. At the same time, the information after the de-noising will be provided to the fault diagnosis system, so that the accuracy of fault diagnosis is improved.
Finally, complex fault diagnosis method based on flexible SCADA is brought forward, as to study the fault diagnosis model for the overall hierarchical information. This method takes full advantage of flexible SCADA interactive features to achieve the stratification of transmission and use for alarm information, which meets the requirement of rapid for simple faults and preciseness for complex faults. As for complex faults, the Petri net forward reasoning model is designed to determine the fault components. When the alarm information is not completely correct, application components protecting configuration of delaying Petri nets is used to correct the error alarm information to improve fault tolerance. Besides that, this paper also made a way to test and verify the correcting alarm information and fault diagnosis using WAMS information. The simulation example proved the effectiveness of the method.
本文首先对信息完备化做了深入的研究,提出建立基于广域网的柔性SCADA系统,对故障信息按重要程度进行分级上传,同时具有主动上传和等待召唤两种方式,为故障诊断提供灵活、有效的数据交换和功能协调的平台。柔性SCADA系统不再是点对点的单向固定路径的信息交互,而是能实现网络中任意两个(或多个)节点间双向的信息交互,体现子站和主站信息交换的智能性,它是对传统SCADA的扩充和发展,拓宽了系统的数据来源,为故障诊断模块丰富了数据基础。
其次,针对电力系统发生大面积复杂故障后,报警信息存在大量的噪声干扰问题,对报警信息处理和智能报警进行了研究,采用数据挖掘的方法,从历史数据中挖掘出规则,去除实时报警信息的噪声,在此基础上,提出用正向产生式推理,优先和分级报警,减少报警信息数量并提高报警质量和正确率。同时,将去噪后的信息提供给故障诊断模块进行故障分析,提高故障诊断信息的正确率。
最后,本文提出了基于柔性SCADA系统的电力系统复杂故障诊断方法,对全局分层信息的电网故障诊断模型进行了研究。充分利用柔性SCADA的交互性特点,实现了报警信息的分层传输和分层利用,满足了简单故障的快速诊断和复杂故障的准确诊断要求。对于复杂故障,设计了应用元件Petri网故障诊断模型正向推理来确定故障元件方法。当报警信息不完全正确时,提出了应用元件保护配置时延Petri网进行报警信息纠错处理方法,提高容错能力。本文还提出了引用WAMS数据对报警信息纠错处理结果和故障诊断结果进行验证的方法,可进一步提高故障诊断的可靠性和正确性。经算例仿真验证了本文方法的有效性。
The issue of fault diagnosis of power system is a kind of research that based on fault comprehensive information and Knowledge-Based System or model, using kind of diagnosis mechanism to determine fault equipments or reason, at the same time appraising the actions of protecting devices, automatic security devices and other such as automatic monitoring and controlling equipments. At present, the fault diagnosis methods still have some problems in overall situation and in practical. There are three main reasons. First, the information is incomplete. Second, the information noise interference is too large. Third, the robustness of diagnostic is too poor while the information is incomplete. To this end, the paper research about complex fault diagnosis method based on the flexible SCADA in power system. A flexible SCADA system that information can exchange is put forward. In the alarm information processing, intelligent alarm system is developed. After that, Petri nets fault diagnosis model based on forward and backward reasoning is put forward, combining the features Petri nets and expert system. The fault information and diagnostic results is verified by using WAMS information.
First of all, the paper did a deep research to complete the information and raised to create the flexible SCADA system based on wide area network, which can classify and upload according to importance of fault information, having initiative and waiting two patterns at the same time, in order to provide a flexible and effective platform of data exchange and functions coordination for fault diagnosis. The flexible SCADA system is no longer a fixed point-to-point one-way path of information exchange, but the network can achieve any two (or more) nodes of information exchange in two-way, which can reflect intelligence of information exchange between master- station and the sub-stations. It is the development and expansion of traditional SCADA, expanding the system's data sources for the fault diagnosis model.
Secondly, as there are a lot of noise problems of warning messages after large-area complex fault in power system, information processing and intelligent alarm was studied in this paper. Noises of real-time alarm information are removed from historical database using data mining methods. On this basis, forward and backward reasoning is put forward to have priority classification alarm and reduce the number of alarm information and improve the quality and accuracy of alarm. At the same time, the information after the de-noising will be provided to the fault diagnosis system, so that the accuracy of fault diagnosis is improved.
Finally, complex fault diagnosis method based on flexible SCADA is brought forward, as to study the fault diagnosis model for the overall hierarchical information. This method takes full advantage of flexible SCADA interactive features to achieve the stratification of transmission and use for alarm information, which meets the requirement of rapid for simple faults and preciseness for complex faults. As for complex faults, the Petri net forward reasoning model is designed to determine the fault components. When the alarm information is not completely correct, application components protecting configuration of delaying Petri nets is used to correct the error alarm information to improve fault tolerance. Besides that, this paper also made a way to test and verify the correcting alarm information and fault diagnosis using WAMS information. The simulation example proved the effectiveness of the method.
引文
[1]王梅义.大电网事故分析与技术应用.北京:中国电力出版社,2008.1~98.
[2]韩水,范舜,张近珠.国外典型电网事故分析.北京:中国电力出版社,2005.13~145.
[3]王士政.电网调度自动化与配网自动化技术(第二版).北京:中国水利水电出版社,2006.25-32.
[4]高翔.数字化变电站应用技术.北京:中国电力出版社,2008.9~12.
[5]张喜林,张子义,王书春,等.地区电力调度EMS报警功能需求及算法.电力系统自动化,2005,29(10):76~78.
[6]程碧祥.电力调度自动化系统中事故告警的探讨.电力信息化,2008,6(3):33~34.
[7] Kirschen D S,Wollenberg B F. Intelligent Alarm Processing in Power System. Proceedings of the IEEE,1992,80(5):663~672.
[8] Prince W R,Wollenberg BF,Bertagnolli D B. Survey of Excessive Alarms.IEEE Transactions on Power Systems,1989,4(3):950~956.
[9] Amelink H,Forte A M,Gberman R P. Dispatcher Alarm an Message Processing. IEEE Transactions on Power Systems,1986,1(3):188~194.
[10] Shoop A H,Silverman S. A Real-Time Alarm Processor. International Journal of Electrical Power & Energy Systems,1992,14(2,3):108~113.
[11] Hein F. Expert System Using Pattern Recognition by Real Time Signals.GIGRE, France:1986.
[12] Chan E H P. Application of Neural-Network Computing in Intelligent Alarm Processor. Proceedings of Power Industry Computer Application Conference (PICA’89),Seattle(USA):1989.
[13]文福拴,韩祯祥.基于覆盖集理论和Tabu搜索方法的电力系统警报处理.电力系统自动化,1997,21(2):18~24.
[14] Zita A. Vale A. Machado e Moura. An expert system with temporal reasoning for alarm processing in power system control centers. IEEE Transactions on Power Systems,1993.8,8(3),1307-1314.
[15] G. Lambert-Torres,Member,E.F. Fonseca,M.P.Coutinho, and R.Rossi.Intelligent Alarm Processing,1-4244-0111-9/06/2006 IEEE.
[16]马煜,易善军,杨宁.东北电网运行信息监视报警系统的开发与应用.东北电力技术,2007,(6):50-52.
[17]梁霆.关于电网调度自动化系统智能报警的探讨.广东电力,2005,18(5):41-43.
[18]毛鹏,许扬,蒋平.输电网故障诊断综述及发展.继电器,2005,33(22):79~86.
[19]张伯明,陈寿孙.高等电力网络分析.北京:清华大学出版社,1996.125~156.
[20]毕天姝,倪以信,杨奇逊.人工智能技术在输电网络故障诊断中的应用述评.电力系统自动化,2000,24(2):11~16
[21] Chihiro Fukui,Junzo Kawakami. an expert system for fault section estimation using information from protective relays and circuit breakers.IEEE Transactions on power delivery,1986,1(4): 83~91.
[22] Ernesto Vazquez M,Oscar L,Chacon M,et al.An online expert system for fault section diagnosis in power systems.IEEE Transaction on power systems, 1997,12(1):357~362
[23]杜一,张沛超,郁惟镛.基于事例和规则混合推理的变电站故障诊断系统.电网技术,2004,28(1):34~37.
[24]刘青松,夏道止.基于正反向推理的电力系统故障诊断专家系统.电网技术,1999,23(9):66~68.
[25] Wake A,Sakaguchi T. Method to determine the fault components of power system based on description of structure and function of relay system.IEE Trans. Japan,1984, 104-B(10):248~256.
[26] Young Moon Park,Gwang-Won Kim,Jin-Man Sohn.A logic based expert system (LBES) for fault diagnosis of power system.IEEE Transactions on power systems. 1997,12(1):363~369.
[27]赵冬梅,郭锐,徐开理,等.电网故障诊断专家系统的一种实现.电力自动化设备,2000,20(4):33~36.
[28] Navarro V ,da Silva A L, Luis A.V. et al. Artificial neural networks for power systems diagnosis.IEEE international conference on neural networks,1994,6:3738~3743.
[29] Z.E. Aygen,S.Seker,M.bagriyanik et.al. Fault section estimation in electrical power systems using artificial neural network approach.in:IEEE Transmission and distribution conference,1999, 2(1)466~469.
[30]毕天姝,倪以信,吴复立,等.基于新型神经网络的电网故障诊断方法.中国电机工程学报,2002,22(2):73~78.
[31]顾雪平,张文勤,高曙,等.基于神经网络和元件关联分析的电网故障诊断.华北电力大学学报,1999,26(2):12~17.
[32] Wangho K,Kim. Application of hierarchical neural networks to fault diagnosis of power systems. International journal of electric power energy system,1993,15(2): 65~70.
[33] Wen F.S. and Han Z.X. A refined genetic algoithm for fault section estimation in power systems using the time sequence information of circuit breakers.Electric Machines and Power Systems, 1996,24(8):801~805.
[34] Wen F.S. and Han Z.X. Fault section estimation power systems using a genetic algorithm.Electric Power Systems Research,1995,34(3):165~172.
[35]文福拴,韩祯祥.基于遗传算法和模拟退火算法的的电力系统故障诊断.中国电机工程学报,1994,14(3):29~35.
[36] Lai L L,Sichanie A G,Gwyn B J.Comparision between evolutionary programming and a genetic algorithm for fault section estimation. IEE proceedings -Generation, transmission and distribution,1998,145(5):616~620.
[37]文福拴,邱家驹,韩祯祥。只利用断路器信息诊断电力系统故障的高级遗传算法。电工技术学报,1996,11(2):58~64.
[38]文福拴,韩祯祥.基于覆盖集理论和Tabu搜索方法的电力系统警报处理.电力系统自动化,1997,21(2):18~23.
[39] Wen F.S. ,Chang C.S. Probabilistic diagnosis theory for fault section estimation and state identification of unobserved protective relay using Tabu search method. IEE Proceedings on Generation, Transmissions and Distribution,1998,145(6):722~730.
[40]文福拴,钱源平,韩祯祥,等.利用保护和断路器信息的电力系统故障诊断与不可观测的保护的状态识别的模型与Tabu搜索算法.电工技术学报,1998,13(5):1~8.
[41] Harald Kirsch,Kristian Kroschel. Applying Bayesian networks to fault diagnosis Proceedings of the third conference on control applications,1994,2:895~ 900.
[42] Chen-Fu Chien,Shi-Lin Chen,Yih-Shin Lin.Using Bayesian network for fault location feeder.IEEE Transactions on power delivery,2002,17(3):785~793.
[43] Zhu Yongli,Huo Limin,Lu Jinling.Bayesian networks-based approach for power systems fault diagnosis.IEEE Transactions on power delivery,2006,21(2):634~639.
[44]吴欣,郭创新,曹一家.基于贝叶斯网络及信息时序属性的电力系统故障诊断方法.中国电机工程学报,2005,25(13):14~18.
[45]赵伟.电力系统多区域复杂故障诊断的研究:[博士学位论文] .北京:中国电力科学研究院,2006.
[46]张耀天,何正友,赵静,等.基于粗糙集理论和朴素贝叶斯网络的电网故障诊断方法.电网技术,2007,31(1):37~41.
[47] Momoh J A,Ma X W,Tomsovic K. Overview and literature survey of fuzzy set theory in power systems. IEEE Transactions on power systems,1995,10(3):1676~1690.
[48] Cho H J,Park J K. An expert system for fault section diagnosis of power systems using fuzzy relations.IEEE Trans on power systems,1997,12(1):342~348.
[49] Mosef H,Ranjbar A M,Jadid S. Fuzzy rule-based expert system for power system fault diagnosis.IEE proceedings—Generation, Transmission and Distribution,1997, 144(2):186~192.
[50] Chang C S,Chen J M,Srinivasan D,et al.Fuzzy logic approach in power system fault section identification. IEE proceedings—Generation, Transmission and Distribution, 1997,144(5):406~ 414.
[51]王刚,魏守智,赵海.基于模糊评判的决策级信息融合算法的研究.计算机工程与应用,2002,38(15):18~19.
[52]徐文,王大忠,周泽存,等.模糊辨识在电力设备故障诊断中的应用.电力系统自动化,1996,20(3):45~49.
[53]张师超,汤服成.基于实例的模糊推理模型.模糊系统与数学,1998,12(1): 71~76.
[54]曾黄麟.粗集理论及其应用.重庆:重庆大学出版社,1998.89~104.
[55]束洪春,孙向飞,于继来.粗糙集理论在电力系统中的应用.电力系统自动化,2004,28(3):90~95.
[56]孙秋野,张化光.基于粗糙集的配电系统连续信号故障诊断方法.中国电机工程学报,2006,26(11):156~161.
[57]孙秋野,张化光,戴璟.基于改进粗糙集约简算法的配电系统再现故障诊断.中国电机工程学报,2007,27(7):58~64.
[58] Peterson J.L.Petri net theory and the modeling of systems. Prentice Hall,Englewood Cliffs,N.J.1981.
[59] Lo K.L,Ng H.S,Trecat J. Power Systems fault diagnosis using Petri nets.IEE Proceedings-Generations, Transmissions and Distributions,1997,144(3):231~236.
[60]王建元,纪延超,常群,等.Petri网络理论在电网故障诊断中的应用.哈尔滨理工大学学报,2002,7(4):77~ 80.
[61]赵洪山,米增强,杨奇逊.基于冗余嵌入Petri网技术的变电站故障诊断.电力系统自动化,2002,26(4):32~ 35.
[62]孙静,秦世引,宋永华.一种基于Petri网和概率信息的电力系统故障诊断方法.电力系统自动化,2003,27(13):10~ 14.
[63] Sun J,Qin S Y,Song Y H.Fault diagnosis of electric power systems based on fuzzy Petri nets.IEEE Transactions on power systems,2004,19(4):2053~2059.
[64]高梅梅,吴智铭.模糊推理Petri网及其在故障诊断中的应用.自动化学报,2000,26(5):677 ~680.
[65]任建文,周明,李庚银.电网故障信息综合分析及管理系统研究.电网技术,2002,26(4):38～41.
[66]杨海晶,张东英,刘文颖,等.电力系统柔性SCADA框架设计及功能分析.电网技术,2006,30(15):36～40.
[67]汪可友,张沛超,郁惟镛,等.应用IEC61850通信协议的新一代故障信息处理系统.电网技术,2004,28(10):55~58.
[68]周明,任建文,李庚银,等.基于多智能体的电网调度操作票指导系统研究与实现.中国电机工程学报,2004,24(4):58~62.
[69]张海波,张伯明,孙宏斌.基于异步迭代的多区域互联系统动态潮流分解协调计算.电力系统自动化,2003,27(24):1~5.
[70]杨以涵,张东英,马骞,等.大电网安全防御体系的基础研究.电网技术,2004,28(9):23~27.
[71] McClanaban R H.SCADA and IP is network convergence really here.IEEE IndustryApplication Magazine,2003,9(2):29~36.
[72]王明俊.我国电网调度自动化的发展—从SCADA到EMS.电网技术,2004,28(4):44~46.
[73]吴文传,张伯明,徐春晖.调度自动化系统实时数据库模型的研究与实现.电网技术,2001,25(9):28~32.
[74]董朝霞,杨峰,范斗.基于知识的多源异构信息一体化研究.电网技术,2004,28(17):67~71.
[75]肖晋宇,谢小荣,李建,等.电网广域动态安全监测系统及其动态模拟试验.电网技术,2004,28(6):5~9.
[76]周士跃,王劲松,金小达.地区供电网调度实时数据网络安全分析及对策.电网技术,2003,27(10):52~55.
[77]孙宏斌,吴文传,张伯明,等.IEC61970标准的扩展在调度控制中心集成化中的应用.电网技术,2005,29(16):22~26.
[78]韩晓萍,李佰国,王肃,等.继电保护及故障信息系统的设计与实现.电网技术,2004,28(18):16~19.
[79]高翔,张沛超,章坚民.电网故障信息系统应用技术.北京:中国电力出版社,2006,107~109.
[80]汪可友,张沛超,郁惟镛,等.应用IEC 61850通信协议的新一代故障信息处理系统.电网技术,2004,28(10):55~58.
[81] T.Kostic. O. Preiss, Ch. Frei. Towards the Formal Integration of Two Upcoming Standards: IEC 61970 and IEC 61850.
[82]章坚民,朱炳铨,赵舫,等.基于IEC 61850的变电站子占系统建模与实现.电力系统自动化,2004,28(21):43~48.
[83]韩晓萍,李佰国,王肃,等.继电保护及故障信息系统的设计与实现.电网技术,2004,28(18):16~19.
[84]高湛军,潘贞存,卞鹏.基于IEC 61850标准的微机保护数据通信模型.电力系统自动化,2003,27(18):43~46.
[85]张慎明,黄海峰.基于IEC 61970标准的电网调度自动化系统体系结构.电力系统自动化,2002:45~47.
[86]王大伟.基于IEC61850的变电站自动化通信体系研究及工程应用:[硕士学位论文] .北京:华北电力大学,2006.
[87]朱明.数据挖掘.北京:中国科学技术大学出版社,2002,98~134.
[88]于之虹,郭志忠.数据挖掘与电力系统.电网技术,2001,25(8):58~62.
[89]蔡志慧,张峰,牛文瑜.提高变电站监控系统信号设置的合理性.电力自动化设备,2005,25(7):96~98.
[90]廖志伟,孙雅明.数据挖掘技术及其在电力系统中的应用.电力系统自动化,2001,25(11):62~66.
[91]潘显民.数据挖掘技术及其应用.湘潭师范学院学报(自然科学版),2007, 29(1):38~40.
[92] G.Piatetsky-Shapiro and W.j.Frawley. Knowledge Discovery in Database. Cambridge , MA: AAAI/MIT Press,1991.
[93] U.M.Fayyad,G.Piatesky-Shapiro,P.Smyth,and R.Uthurusamy, editors. Advances in Knowledge Discovery and Data Mining .Cambridge, MA:AAAI/MIT Press,1996.
[94] S.M Weiss and N.Indurkhya. Predictive Data Ming. San Francisco: Morgan Kaufmann. 1998.
[95]蒋昌俊.Petri网的行为理论及其应用.北京:高等教育出版社,2003.
[96]袁崇义.Petri网原理.北京:电子工业出版社,1998.
[97]陈仕兵,贾再一.用Petri网模型进行电力系统结线分析.微机发展,2000,12(6):14～16.
[98] Jensen,K.High-level Petri nets:Application and theory of Petri net. Informatik Fachberichte,1998,3,Vol 66.
[99] Ramchandani C.Analysis of asynchronous concurrent systems by timed Petri nets. Massachusetts institute of technology. Project MAC, technology report 120, 1974.
[100] Merlin P,Faber D J.Recoverability of communication protocols.IEEE Transactions on communications,1976,Vol 24,No 9.
[101]王建元,纪延超.Petri网络在变压器故障诊断中的应用.电网技术,2002,26(8):21~24.
[102]许君德.地区电网故障诊断实用化研究与实现:[硕士学位论文] .北京:华北电力大学,2006.
[103]孙静,秦世引,宋永华.一种基于Petri网和概率信息的电力系统故障诊断方法.电力系统自动化,2003,27(13):10~14.
[104] Sang W M,Jong K P, K wang H K,et al. A fuzzy relation based fault section diagnosis mehtod for Power systems using operating sequences of Protective devices. IEEE Power Engineering Society Summer Meeting,2001(2):933~938.
[105]贺家李,宋从矩.电力系统继电保护原理(第三版) .北京:水利电力出版社,1994.
[106]文福拴,韩祯祥.基于遗传算法和模拟退火算法的电力系统的故障诊断.中国电机工程学报,1994,14(3):29~35.
[107]吴欣.基于改进贝叶斯网络方法的电力系统故障诊断研究:[硕士学位论文].杭州:浙江大学,2006.
[108]彭疆南,孙元章,王海风.考虑系统完全可观测性的PMU最优配置方法.电力系统自动化,2003,27(4):10~16.
[2]韩水,范舜,张近珠.国外典型电网事故分析.北京:中国电力出版社,2005.13~145.
[3]王士政.电网调度自动化与配网自动化技术(第二版).北京:中国水利水电出版社,2006.25-32.
[4]高翔.数字化变电站应用技术.北京:中国电力出版社,2008.9~12.
[5]张喜林,张子义,王书春,等.地区电力调度EMS报警功能需求及算法.电力系统自动化,2005,29(10):76~78.
[6]程碧祥.电力调度自动化系统中事故告警的探讨.电力信息化,2008,6(3):33~34.
[7] Kirschen D S,Wollenberg B F. Intelligent Alarm Processing in Power System. Proceedings of the IEEE,1992,80(5):663~672.
[8] Prince W R,Wollenberg BF,Bertagnolli D B. Survey of Excessive Alarms.IEEE Transactions on Power Systems,1989,4(3):950~956.
[9] Amelink H,Forte A M,Gberman R P. Dispatcher Alarm an Message Processing. IEEE Transactions on Power Systems,1986,1(3):188~194.
[10] Shoop A H,Silverman S. A Real-Time Alarm Processor. International Journal of Electrical Power & Energy Systems,1992,14(2,3):108~113.
[11] Hein F. Expert System Using Pattern Recognition by Real Time Signals.GIGRE, France:1986.
[12] Chan E H P. Application of Neural-Network Computing in Intelligent Alarm Processor. Proceedings of Power Industry Computer Application Conference (PICA’89),Seattle(USA):1989.
[13]文福拴,韩祯祥.基于覆盖集理论和Tabu搜索方法的电力系统警报处理.电力系统自动化,1997,21(2):18~24.
[14] Zita A. Vale A. Machado e Moura. An expert system with temporal reasoning for alarm processing in power system control centers. IEEE Transactions on Power Systems,1993.8,8(3),1307-1314.
[15] G. Lambert-Torres,Member,E.F. Fonseca,M.P.Coutinho, and R.Rossi.Intelligent Alarm Processing,1-4244-0111-9/06/2006 IEEE.
[16]马煜,易善军,杨宁.东北电网运行信息监视报警系统的开发与应用.东北电力技术,2007,(6):50-52.
[17]梁霆.关于电网调度自动化系统智能报警的探讨.广东电力,2005,18(5):41-43.
[18]毛鹏,许扬,蒋平.输电网故障诊断综述及发展.继电器,2005,33(22):79~86.
[19]张伯明,陈寿孙.高等电力网络分析.北京:清华大学出版社,1996.125~156.
[20]毕天姝,倪以信,杨奇逊.人工智能技术在输电网络故障诊断中的应用述评.电力系统自动化,2000,24(2):11~16
[21] Chihiro Fukui,Junzo Kawakami. an expert system for fault section estimation using information from protective relays and circuit breakers.IEEE Transactions on power delivery,1986,1(4): 83~91.
[22] Ernesto Vazquez M,Oscar L,Chacon M,et al.An online expert system for fault section diagnosis in power systems.IEEE Transaction on power systems, 1997,12(1):357~362
[23]杜一,张沛超,郁惟镛.基于事例和规则混合推理的变电站故障诊断系统.电网技术,2004,28(1):34~37.
[24]刘青松,夏道止.基于正反向推理的电力系统故障诊断专家系统.电网技术,1999,23(9):66~68.
[25] Wake A,Sakaguchi T. Method to determine the fault components of power system based on description of structure and function of relay system.IEE Trans. Japan,1984, 104-B(10):248~256.
[26] Young Moon Park,Gwang-Won Kim,Jin-Man Sohn.A logic based expert system (LBES) for fault diagnosis of power system.IEEE Transactions on power systems. 1997,12(1):363~369.
[27]赵冬梅,郭锐,徐开理,等.电网故障诊断专家系统的一种实现.电力自动化设备,2000,20(4):33~36.
[28] Navarro V ,da Silva A L, Luis A.V. et al. Artificial neural networks for power systems diagnosis.IEEE international conference on neural networks,1994,6:3738~3743.
[29] Z.E. Aygen,S.Seker,M.bagriyanik et.al. Fault section estimation in electrical power systems using artificial neural network approach.in:IEEE Transmission and distribution conference,1999, 2(1)466~469.
[30]毕天姝,倪以信,吴复立,等.基于新型神经网络的电网故障诊断方法.中国电机工程学报,2002,22(2):73~78.
[31]顾雪平,张文勤,高曙,等.基于神经网络和元件关联分析的电网故障诊断.华北电力大学学报,1999,26(2):12~17.
[32] Wangho K,Kim. Application of hierarchical neural networks to fault diagnosis of power systems. International journal of electric power energy system,1993,15(2): 65~70.
[33] Wen F.S. and Han Z.X. A refined genetic algoithm for fault section estimation in power systems using the time sequence information of circuit breakers.Electric Machines and Power Systems, 1996,24(8):801~805.
[34] Wen F.S. and Han Z.X. Fault section estimation power systems using a genetic algorithm.Electric Power Systems Research,1995,34(3):165~172.
[35]文福拴,韩祯祥.基于遗传算法和模拟退火算法的的电力系统故障诊断.中国电机工程学报,1994,14(3):29~35.
[36] Lai L L,Sichanie A G,Gwyn B J.Comparision between evolutionary programming and a genetic algorithm for fault section estimation. IEE proceedings -Generation, transmission and distribution,1998,145(5):616~620.
[37]文福拴,邱家驹,韩祯祥。只利用断路器信息诊断电力系统故障的高级遗传算法。电工技术学报,1996,11(2):58~64.
[38]文福拴,韩祯祥.基于覆盖集理论和Tabu搜索方法的电力系统警报处理.电力系统自动化,1997,21(2):18~23.
[39] Wen F.S. ,Chang C.S. Probabilistic diagnosis theory for fault section estimation and state identification of unobserved protective relay using Tabu search method. IEE Proceedings on Generation, Transmissions and Distribution,1998,145(6):722~730.
[40]文福拴,钱源平,韩祯祥,等.利用保护和断路器信息的电力系统故障诊断与不可观测的保护的状态识别的模型与Tabu搜索算法.电工技术学报,1998,13(5):1~8.
[41] Harald Kirsch,Kristian Kroschel. Applying Bayesian networks to fault diagnosis Proceedings of the third conference on control applications,1994,2:895~ 900.
[42] Chen-Fu Chien,Shi-Lin Chen,Yih-Shin Lin.Using Bayesian network for fault location feeder.IEEE Transactions on power delivery,2002,17(3):785~793.
[43] Zhu Yongli,Huo Limin,Lu Jinling.Bayesian networks-based approach for power systems fault diagnosis.IEEE Transactions on power delivery,2006,21(2):634~639.
[44]吴欣,郭创新,曹一家.基于贝叶斯网络及信息时序属性的电力系统故障诊断方法.中国电机工程学报,2005,25(13):14~18.
[45]赵伟.电力系统多区域复杂故障诊断的研究:[博士学位论文] .北京:中国电力科学研究院,2006.
[46]张耀天,何正友,赵静,等.基于粗糙集理论和朴素贝叶斯网络的电网故障诊断方法.电网技术,2007,31(1):37~41.
[47] Momoh J A,Ma X W,Tomsovic K. Overview and literature survey of fuzzy set theory in power systems. IEEE Transactions on power systems,1995,10(3):1676~1690.
[48] Cho H J,Park J K. An expert system for fault section diagnosis of power systems using fuzzy relations.IEEE Trans on power systems,1997,12(1):342~348.
[49] Mosef H,Ranjbar A M,Jadid S. Fuzzy rule-based expert system for power system fault diagnosis.IEE proceedings—Generation, Transmission and Distribution,1997, 144(2):186~192.
[50] Chang C S,Chen J M,Srinivasan D,et al.Fuzzy logic approach in power system fault section identification. IEE proceedings—Generation, Transmission and Distribution, 1997,144(5):406~ 414.
[51]王刚,魏守智,赵海.基于模糊评判的决策级信息融合算法的研究.计算机工程与应用,2002,38(15):18~19.
[52]徐文,王大忠,周泽存,等.模糊辨识在电力设备故障诊断中的应用.电力系统自动化,1996,20(3):45~49.
[53]张师超,汤服成.基于实例的模糊推理模型.模糊系统与数学,1998,12(1): 71~76.
[54]曾黄麟.粗集理论及其应用.重庆:重庆大学出版社,1998.89~104.
[55]束洪春,孙向飞,于继来.粗糙集理论在电力系统中的应用.电力系统自动化,2004,28(3):90~95.
[56]孙秋野,张化光.基于粗糙集的配电系统连续信号故障诊断方法.中国电机工程学报,2006,26(11):156~161.
[57]孙秋野,张化光,戴璟.基于改进粗糙集约简算法的配电系统再现故障诊断.中国电机工程学报,2007,27(7):58~64.
[58] Peterson J.L.Petri net theory and the modeling of systems. Prentice Hall,Englewood Cliffs,N.J.1981.
[59] Lo K.L,Ng H.S,Trecat J. Power Systems fault diagnosis using Petri nets.IEE Proceedings-Generations, Transmissions and Distributions,1997,144(3):231~236.
[60]王建元,纪延超,常群,等.Petri网络理论在电网故障诊断中的应用.哈尔滨理工大学学报,2002,7(4):77~ 80.
[61]赵洪山,米增强,杨奇逊.基于冗余嵌入Petri网技术的变电站故障诊断.电力系统自动化,2002,26(4):32~ 35.
[62]孙静,秦世引,宋永华.一种基于Petri网和概率信息的电力系统故障诊断方法.电力系统自动化,2003,27(13):10~ 14.
[63] Sun J,Qin S Y,Song Y H.Fault diagnosis of electric power systems based on fuzzy Petri nets.IEEE Transactions on power systems,2004,19(4):2053~2059.
[64]高梅梅,吴智铭.模糊推理Petri网及其在故障诊断中的应用.自动化学报,2000,26(5):677 ~680.
[65]任建文,周明,李庚银.电网故障信息综合分析及管理系统研究.电网技术,2002,26(4):38～41.
[66]杨海晶,张东英,刘文颖,等.电力系统柔性SCADA框架设计及功能分析.电网技术,2006,30(15):36～40.
[67]汪可友,张沛超,郁惟镛,等.应用IEC61850通信协议的新一代故障信息处理系统.电网技术,2004,28(10):55~58.
[68]周明,任建文,李庚银,等.基于多智能体的电网调度操作票指导系统研究与实现.中国电机工程学报,2004,24(4):58~62.
[69]张海波,张伯明,孙宏斌.基于异步迭代的多区域互联系统动态潮流分解协调计算.电力系统自动化,2003,27(24):1~5.
[70]杨以涵,张东英,马骞,等.大电网安全防御体系的基础研究.电网技术,2004,28(9):23~27.
[71] McClanaban R H.SCADA and IP is network convergence really here.IEEE IndustryApplication Magazine,2003,9(2):29~36.
[72]王明俊.我国电网调度自动化的发展—从SCADA到EMS.电网技术,2004,28(4):44~46.
[73]吴文传,张伯明,徐春晖.调度自动化系统实时数据库模型的研究与实现.电网技术,2001,25(9):28~32.
[74]董朝霞,杨峰,范斗.基于知识的多源异构信息一体化研究.电网技术,2004,28(17):67~71.
[75]肖晋宇,谢小荣,李建,等.电网广域动态安全监测系统及其动态模拟试验.电网技术,2004,28(6):5~9.
[76]周士跃,王劲松,金小达.地区供电网调度实时数据网络安全分析及对策.电网技术,2003,27(10):52~55.
[77]孙宏斌,吴文传,张伯明,等.IEC61970标准的扩展在调度控制中心集成化中的应用.电网技术,2005,29(16):22~26.
[78]韩晓萍,李佰国,王肃,等.继电保护及故障信息系统的设计与实现.电网技术,2004,28(18):16~19.
[79]高翔,张沛超,章坚民.电网故障信息系统应用技术.北京:中国电力出版社,2006,107~109.
[80]汪可友,张沛超,郁惟镛,等.应用IEC 61850通信协议的新一代故障信息处理系统.电网技术,2004,28(10):55~58.
[81] T.Kostic. O. Preiss, Ch. Frei. Towards the Formal Integration of Two Upcoming Standards: IEC 61970 and IEC 61850.
[82]章坚民,朱炳铨,赵舫,等.基于IEC 61850的变电站子占系统建模与实现.电力系统自动化,2004,28(21):43~48.
[83]韩晓萍,李佰国,王肃,等.继电保护及故障信息系统的设计与实现.电网技术,2004,28(18):16~19.
[84]高湛军,潘贞存,卞鹏.基于IEC 61850标准的微机保护数据通信模型.电力系统自动化,2003,27(18):43~46.
[85]张慎明,黄海峰.基于IEC 61970标准的电网调度自动化系统体系结构.电力系统自动化,2002:45~47.
[86]王大伟.基于IEC61850的变电站自动化通信体系研究及工程应用:[硕士学位论文] .北京:华北电力大学,2006.
[87]朱明.数据挖掘.北京:中国科学技术大学出版社,2002,98~134.
[88]于之虹,郭志忠.数据挖掘与电力系统.电网技术,2001,25(8):58~62.
[89]蔡志慧,张峰,牛文瑜.提高变电站监控系统信号设置的合理性.电力自动化设备,2005,25(7):96~98.
[90]廖志伟,孙雅明.数据挖掘技术及其在电力系统中的应用.电力系统自动化,2001,25(11):62~66.
[91]潘显民.数据挖掘技术及其应用.湘潭师范学院学报(自然科学版),2007, 29(1):38~40.
[92] G.Piatetsky-Shapiro and W.j.Frawley. Knowledge Discovery in Database. Cambridge , MA: AAAI/MIT Press,1991.
[93] U.M.Fayyad,G.Piatesky-Shapiro,P.Smyth,and R.Uthurusamy, editors. Advances in Knowledge Discovery and Data Mining .Cambridge, MA:AAAI/MIT Press,1996.
[94] S.M Weiss and N.Indurkhya. Predictive Data Ming. San Francisco: Morgan Kaufmann. 1998.
[95]蒋昌俊.Petri网的行为理论及其应用.北京:高等教育出版社,2003.
[96]袁崇义.Petri网原理.北京:电子工业出版社,1998.
[97]陈仕兵,贾再一.用Petri网模型进行电力系统结线分析.微机发展,2000,12(6):14～16.
[98] Jensen,K.High-level Petri nets:Application and theory of Petri net. Informatik Fachberichte,1998,3,Vol 66.
[99] Ramchandani C.Analysis of asynchronous concurrent systems by timed Petri nets. Massachusetts institute of technology. Project MAC, technology report 120, 1974.
[100] Merlin P,Faber D J.Recoverability of communication protocols.IEEE Transactions on communications,1976,Vol 24,No 9.
[101]王建元,纪延超.Petri网络在变压器故障诊断中的应用.电网技术,2002,26(8):21~24.
[102]许君德.地区电网故障诊断实用化研究与实现:[硕士学位论文] .北京:华北电力大学,2006.
[103]孙静,秦世引,宋永华.一种基于Petri网和概率信息的电力系统故障诊断方法.电力系统自动化,2003,27(13):10~14.
[104] Sang W M,Jong K P, K wang H K,et al. A fuzzy relation based fault section diagnosis mehtod for Power systems using operating sequences of Protective devices. IEEE Power Engineering Society Summer Meeting,2001(2):933~938.
[105]贺家李,宋从矩.电力系统继电保护原理(第三版) .北京:水利电力出版社,1994.
[106]文福拴,韩祯祥.基于遗传算法和模拟退火算法的电力系统的故障诊断.中国电机工程学报,1994,14(3):29~35.
[107]吴欣.基于改进贝叶斯网络方法的电力系统故障诊断研究:[硕士学位论文].杭州:浙江大学,2006.
[108]彭疆南,孙元章,王海风.考虑系统完全可观测性的PMU最优配置方法.电力系统自动化,2003,27(4):10~16.