电力机车系统电磁暂态过程研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
摘要:电力机车是铁路运输的关键设备之一,机车频繁变换各种状态,常处在不同的暂态过程中。强烈的电磁暂态过程,会引起高幅值的冲击电压或冲击电流,导致电力机车上变流、变频装置的可控晶闸管击穿,吸收电容器损坏,甚至由电力机车变压器耦合作用致使变压器原边产生极大的过电压,导致车顶避雷器放电,接触网绝缘击穿,牵引变电所跳闸,电力机车失去牵引电源不能行驶,严重地影响到铁路的正常运输,是电力机车运行中碰到的一大难题,因此对机车不同的暂态过程进行研究为解决实际问题提供了理论基础,保证了机车的安全运行。
     牵引电机的磁化曲线是直流牵引电机准确建模及性能分析的基础,本文以SS型电力机车为例,提出了利用贝叶斯最小二乘支持向量机(LS-SVM)对直流牵引电机磁化曲线进行拟合,从而建立准确的直流牵引电机模型的方法。该方法解决了传统LS-SVM采用交叉验证确定模型参数耗时长的问题。按照变流器牵引PWM控制方法的不同,分别对两电平SPWM及三电平SPWM的牵引控制方法进行了分析,并在此基础上建立了CRH1及CRH2的动态仿真模型。
     针对电力机车牵引电机电流脉动的特点,推导出机车异常运行、再生颠覆及隔离开关误动作时系统暂态电流、电压的数学解析表达式。对机车异常运行、再生颠覆及隔离开关误动作时的暂态过程进行计算和仿真,最后通过实验对理论及仿真结果进行验证。
     利用暂态计算方法将电力机车惰行通过电分相的三个暂态过程中机车主变压器、受电弓产生的暂态过电压及励磁涌流进行计算和仿真,现场实测结果验证了理论分析及仿真计算的正确性。
     利用模态分析法,对牵引网络节点导纳矩阵的特征根进行分析,从而得出运动负荷牵引电网谐波谐振的产生与频率及机车运行位置的关系。理论分析及现场实测均证明该方法是进行运动负荷牵引网谐波谐振分析的有效工具。
     通过对机车几种典型的暂态过程的研究,找出了机车强烈电磁暂态过程的产生机理,分析了暂态过程产生的过电流、过电压对电力机车及牵引系统的影响,为机车的安全运行及系统设计提供了理论支持。
ABSTRACT: Locomotive is one of the key equipments in railway transport. Locomotive often undergo different transient processes when it change its operation states. Intensive electromagnetic transient process will lead to high-amplitude impulse voltage or current which will deduce the thyristor arcing and capacitance destroy. Over-voltage will also occurred in the fist side of transformer because of the coupling which can deduce arrester discharging and insulation arcing. Locomotive can not run because of the tripping in electric power substation. It influences the normal transport seriously. Study on the different transient processes supply the theory foundation to resolve the practical problem and guarantee the safety running of locomotive.
     Magnetization curve is the basis of modeling the traction motors correctly. Make SS series AC-DC Locomotive as instance build dynamic model of locomotive. The dissertation presents bayesian least squares support vector machine (LS-SVM) as a new tool fitting magnetization curve which can solve the problem of time-consuming that traditional LS-SVM model parameters determined by cross validation. The dissertation analyzes the two-level SPWM and three-level SPWM separately and build the dynamic model of CRH1 and CRH2.
     Considering the pulsating current in the thyristor driven dc motor, transient process of abnormal operation and failure of regenerative braking is modeled and analyzed. Mathematic expressions on transient process of the abnormal operation and failure of regenerative braking are deduced. Making SS7 as an example, compute the transient process of abnormal operation and failure of regenerative braking. Using the dynamic simulation model simulate the two transient processes. Experiment was done to verify the analysis and simulation.
     The process of locomotive slipping through articulated phase insulator can be divided in three transient processes. The transient voltage on articulated phase insulator and inrush current are deduced and calculated using electromagnetic transient mathematic method. Field survey verifies the correctness of theory analysis and simulation.
     Modal analysis is proposed to study the characteristic root of the network admittance matrix. Using this method, one can find the relationship between harmonic resonance of traction system and frequency and locomotive position. Analytical and case study have confirmed that the proposed method is a valuable tool for traction power system with moving load.
     Through the study on typical transient processes, the reasons how the intensive electromagnetic transient process will occur are found out. Analyze the over-current and over-voltage influence on the locomotive and traction system. It supplies the theory support to the safety running and system design.
引文
[1]闫跃宣,徐凤章.接触网[M].北京.中国铁道出版社.1992:10
    [2]于万聚.高速电气化铁路接触网[M].成都.西南交通大学出版社.2003,7:163-185
    [3]A Ben Tal, Shein D. Study ferroresonance in actual power systems by bifurcation agram[J]. Electric Power Systems Research,1999, (49):175-183
    [4]Marti J R. Soudack A C Ferroresonance in power systems:fundamental solutions[J]. IEE Proeedings-Generation. Transmission and Distribu-tion.1991.138(4):321-329.
    [5]Jacobson D A N Examples of ferroresonane in a high voltage power system [C]. IEEE Power Engineering Society Ge neral Meting,2003:1206-1212.
    [6]Zia Emin, Bashar A t. Al Zahawi. Quantification of thechaotic behavior of ferroresonant voltage transformer circuits[J]. IEEE Trans on Fundamental Theory and Applications,2001, 48(6):757-759
    [7]Jones, A.J.; Margetts, S.; Durrant, P.; Tsui, A.P.M.; Non-linear modelling and chaotic neural networks[J]. Neural Networks,2000. Proceedings. Sixth Brazilian Symposium on.2000:7-14
    [8]Anhui Liang; Yanfeng Xu; Shouqing Jia;et al. Neural networks for nonlinear modeling of microwave Schottky diodes[C]. Microwave and Millimeter Wave Technology,2008. ICMMT 2008. International Conference on.Nanjing.2008,2:558-561
    [9]Inst.of Naval Autom., Genova, Italy; Nonlinear modeling of complex large-scale plants using neural networks and stochastic approximation[J]. Systems, Man and Cybernetics, Part A, IEEE Transactions on.1997,27(6):750-757.
    [10]Kelouwani, S.; Agbossou, K.; Nonlinear model identification of wind turbine with a neural network[J]. Energy Conversion, IEEE Transaction on.2004,19(3):607-612
    [11]朱玉华.神经网络在非线性系统建模中的应用.石油化工自动化[J].2005,2:43-44
    [12]赵懿,王先来.在非线性建模中神经网络模型的构建与选择[J].计算机应用.2005,25(1):20-24
    [13]薛福珍,柏洁.基于先验知识和神经网络的非线性建模与预测控制[J].系统仿真学报.2004,16(5):1057-1059
    [14]郁俊莉.基于混沌时间序列的非线性动态系统神经网络建模与预测[J].武汉大学学报.2005,51(3):286-290
    [15]王雪飞.一种基于神经网络的非线性时变系统仿真建模方法[J].计算机研究与发展.2006,43(7):1167-1172
    [16]赖桂文,王永初.神经网络在线投影算法及非线性建模应用[J].控制工程.2009.16(2):191-194
    [17]杨先有,易灵芝,段斌,彭寒梅.开关磁阻电机调速系统BP神经网络建模[J].电机与控制学报.2008,12(4):447-450
    [18]Willis, M.J.; Montague, GA.; Di Massimo, C.; et al. Artificial neural networks and their application in process engineering[J]. Neural Networks for Systems: Principles and Applications, IEE Colloquium on.1991,7/1-7/4.
    [19]Elmas, C.; Sagiroglu, S.; Colak, I.;et al. Nonlinear modelling of a switched reluctance drive based on neural networks[C]. Electrotechnical Conference,1994. Proceedings.,7th Mediterranean. Antalya.1994,2:809-812
    [20]Alessandri, A.; Parisini, T.; Nonlinear modelling and state estimation in a real power plant using neural networks and stochastic approximation[C]. Proceedings of the American Control Conference.1995,3:1561-1567
    [21]El-Arabawy, I.F.; Yousef, H.A.; Mostafa, M.Z.;et al. Parameters estimation of nonlinear models of DC motors using neural networks[C]. Industrial Electronics Society,2000. IECON 2000.26th Annual Confjerence of the IEEE.2000,3:1997-2000
    [22]Ljung, L.; Black-box models from input-output measurements[C]. Instrumentation and Measurement Technology Conference,2001. IMTC 2001. Proceedings of the 18th IEEE. Budapest.2001,1:138-146
    [23]金宏,张洪钱.基本样条循环神经网络及其非线性建模[J].控制与决策.1999,14(5):469-472
    [24]刘霞,刘铁男.基于小波神经网络的非线性系统建模研究[J].自动化技术与应用.2004,23(2):22-24
    [25]Yang Jianxi; Zhou Jianting; Wang Fan; A Study on the Application of GA-BP Neural Network in the Bridge Reliability Assessment[C]. Computational Intelligence and Security,2008. CIS '08. International Conference on. Suzhou.2008,1:540-545
    [26]Taktak,A.;Eleuteri,A.;Aung, M.; External Validation of a Bayesian Neural Network Model in Survival Analysis[C]. San Diego, CA.2008:607-612
    [27]Miao Zhenjiang; Yuan Baozong; An extended BAM neural network model[C]. Neural Networks, 1993. IJCNN'93-Nagoya. Proceedings of 1993 International Joint Conference on..1993,3:2682-2685
    [28]Kimura, S.; Sonoda, K.; Yamane, S.; Inference of genetic networks using neural network models[J]. Evolutionary Computation,2005. The 2005 IEEE Congress on.2005,2:1738-1745
    [29]刘妹琴,沈轶.一类新的RBF神经网络在非线性系统建模中的应用[J].控制与决策.2001,16(3):277-281
    [30]张健,康景利,王闽南.基于RBF网络的柴油机非线性建模[J].内燃机学报.2003,21(3):221-226
    [31]王合修,曹留柱.RBF神经网络在开关磁阻电机建模中的应用[J].煤炭技术.2008,27(8):4647
    [32]何谦,李宏光.非线性系统建模的复合型模糊神经网络研究[J].北京化工大学学报:自然科学版.2000,27(4):67-71
    [33]李迎春,申东日,陈义俊.基于模糊神经网络的非线性系统建模方法[J].石油化工自动化.2003,1:27-29
    [34]李冬梅,伞冶.连续非线性动态系统建模的模糊神经网络方法[J].控制与决策.2003,18(6):661-666
    [35]殷娜,李顺林.基于模糊神经网络的非线性动态系统建模[J].兰州交通大学学报.2005,24(4):88-91
    [36]孙强,程明.基于模糊神经网络的双凸极永磁电机非线性建模[J].控制理论与应用.2007,24(4):601-606
    [37]胡玉玲,曹建国.基于模糊神经网络的动态非线性系统辨识研究[J].系统仿真学报. 2007,19(3):560-562.
    [38]Vapnik V.The Nature of Statistical Learning Theory[M].Translated by Zhang Xuegong. Beijing: Tsinghua University Press,2000:91-108
    [39]Wang L P(Ed.).Support Vector Machines:Theory and Application[M]. New York:Springer, Berlin Heidelberg,2005:51-123.
    [40]Feng Cai; Cherkassky, V.; SVM+ regression and multi-task learning[C]. Neural Networks, 2009. IJCNN 2009. International Joint Conference on.2009:418-424
    [41]Kuan-Ming Lin; Chih-Jen Lin; A study on reduced support vector machines[J]. Neural Networks, IEEE Transactions on..2003,14(6):1449-1459
    [42]陈浩,陈立辉,毕笃彦,毛柏鑫.BP网络和支持向量机在非线性函数逼近中的应用[J].航空计算技术.2004,3:27-30
    [43]赵吉文,刘永斌,苏亚辉等.新型直线电机支持向量机非线性建模研究[J].光学精密工程.2006,14(3):450-455
    [44]荣海娜,张葛祥,张翠芳.基于支持向量机的非线性系统辨识方法[J].四川大学学报:自然科学版.2004,z1:447-451
    [45]马振平,孟辉,安金龙.基于SVM的复杂非线性黑箱系统在线辨识方法[J].河北工业大学学报.2006,35(1):6-11
    [46]张莉,席裕庚.基于支持向量机的可分离非线性动态系统辨识[J].自动化学报.2005,31(6):965-969
    [47]张明光,阎威武,李战明.基于支持向量机的非线性系统辨识研究[J].计算机应用研究.2006,23(5):4748
    [48]车畅,胡丹,彭宏.线性规划支持向量机在非线性系统辨识中的应用[J].西南民族大学学报:自然科学版.2006,5:1007-1011
    [49]韩建民,王丽侠,张浩然.基于鲁棒支持向量机的非线性系统辨识[J].仪器仪表学报.2006,z3:2279-2280
    [50]吴德会.基于支持向量机的非线性动态系统辨识方法[J].系统仿真学报.2007,19(14):3169-3171
    [51]袁斌,耿伯英,杨红梅等.基于支持向量机的非线性系统辨识[J].自动化技术与应用.2007,1:23-26
    [52]荣海娜,张葛祥,金炜东.系统辨识中支持向量机核函数及其参数的研究[J].系统仿真学报.2006,18(11):3204-3208
    [53]李明山,王正明,张仪.基于均匀试验设计的支持向量回归参数选择方法[J].系统仿真学报.2008,20(8):2195-2199
    [54]王群京,鲍晓华,倪有源.基于支持向量机和遗传算法的爪极发电机建模及参数优化[J].电工技术学报.2006,21(4):57-61
    [55]袁玉萍,胡亮,周志坚.基于遗传算法对支持向量机模型中参数优化[J].计算机工程与设计.2008,29(19):5016-5018
    [56]Suykens J A K, Vandewalle J.Least Squares Support Vector Machines[J]. Neurel Processing Letters(s 1270-4621),1999,9(3):293-300.
    [57]相征,张太镒,孙建成.基于最小二乘支持向量机的非线性系统建模[J].系统仿真学报.2006,18(9):2684-2687
    [58]徐军辉,汪立新,钱培贤.基于最小二乘支持向量机的小样本建模方法研究[J].航天控 制.2008,26(1):8-12
    [59]陈爱军,宋执环,李平.基于矢量基学习的最小二乘支持向量机建模[J].控制理论与应用.2007,24(1):1-5
    [60]宋海鹰,桂卫华,阳春华.稀疏最小二乘支持向量机及其应用研究[J].信息与控制.2008,37(3):334-338
    [61]李卫,杨煜普,王娜.基于核模糊聚类的多模型LSSVM回归建模[J].控制与决策.2008,23(5):560-562
    [62]宋海鹰,桂卫华,阳春华.模糊偏最小二乘支持向量机的应用研究[J].系统仿真学报.2008,20(5):1344-1347
    [63]崔万照,朱长纯,保文星.最小二乘小波支持向量机在非线性系统辨识中的应用[J].西安交通大学学报.2004,38(6):562-565
    [64]Dong Seong Kim, Ha-Nam Nguyen, Jong Sou Park.. Genetic algorithm to improve SVM based network intrusion detection system[C]. Advanced Information Networking and Applications, 2005. AINA2005.19th International Conference on.2005,2:155-158
    [65]陶少辉,陈德钊,胡望明.LSSVM过程建模中超参数选取的梯度优化算法[J].化工学报.2007,58(6):1514-1517
    [66]Van Gestel T, Suykens JAK, Baestaens DK, et al. Least Squares Support Vector Machines[M]. Singapore:World Scientific,2002.
    [67]Van Gestel T,Suykens JAK, Lanckriet G, et al. Bayesian framework for least squares support vector machine classifiers, Gaussian process and kernel fisher discriminant analysis[J]. Neural Computation,2002,14:1115-1147.
    [68]MacKay D. Bayesian Modeling and Neural Networks. Pasadena: Institute of Technology,1991
    [69]Kwork J T. The evidence framework applied to support vector machines. IEEE Transactions on Neural Networks.2000..11(5):1162-1173
    [70]Law M H. Kwork J T. Appling the Bayesian evidence framework to v-support vector regression//Proceedings of the Twelfth European Conference on Machine Learning. Freiburg. Germany,2001:312-323
    [71]陈磊,张土乔.基于贝叶斯最小二乘支持向量机的时用水量预测模型[J].天津大学学报.2006,39(9):1037-1042
    [72]罗志勇,史忠科.贝叶斯框架支持向量机的模拟电路故障诊断[J].系统仿真学报.2007,19(13):3009-3013
    [73]阎威武,常俊林,邵惠鹤.一种贝叶斯证据框架下支持向量机建模方法的研究[J].控制与决策.2004,19(5):525-528
    [74]邓学寿.CRH2型200km/h动车组牵引传动系统[J].机车电传动.2008,4:1-7
    [75]姜东杰.CRH3型动车组牵引传动系统[J].铁道机车车辆.2008,28(B12):95-99
    [76]胡学永,邓学寿.CRH2型200km/h动车组辅助供电系统[J].机车电传动.2008,5:1-7
    [77]任晋旗,葛琼璇,李耀华.CRH2型动车组牵引系统电机控制策略研究[J].铁道机车车辆.2008,28(B12):109-114
    [78]Richard M. Stephan. A Simple Model for a Thristor Driven dc Motor Considering Continuous and Discontinuous current modes[J]. IEEE Transactions on Education.1991:34 (4).330-335
    [79]Zhang Chuanwei.Bai Zhifeng,Cao Binggang, Li Jingcheng. Study on Regenerative Braking of Electric Vehicle[J]. Power Electronics and Motion Control Conference,2004.IPEMC. August 14-16,2004 Xi'an, China.836-839
    [80]吴榕.SS7电力机车再生制动的运用分析[J].铁道运营技术.2008,1:39-41
    [81]李汝军,吴树强.浅谈电力机车再生制动对馈线保护的影响[J].电气化铁道.2000,2:5-8
    [82]郑树选,李中浩.8K型电力机车的功率因数补偿及其在再生制动工况的特殊性[J].机车电传动.1989,5:5-13
    [83]Strzelecki, R.,Benysek G, Frackowiak L. Dynamic properties of hybrid filters in regenerative braking thyristor systems[C]. Industrial Electronics,1996. ISIE'96., Proceedings of the IEEE International Symposium on. Warsaw.1996,2:612-617.
    [84]Zhang Chuanwei; Bai Zhifeng; Cao Binggang; et.al. Study on regenerative braking of electric vehicle[C]. Power Electronics and Motion Control Conference,2004. IPEMC 2004. The 4th International.2004,2:836-839
    [85]Hill, R.J.; Cai, Y.; Case, S.H.; Irving, M.R.; Iterative techniques for the solution of complex DC-rail-traction systems including regenerative braking[J]. Iterative techniques for the solution of complex DC-rail-traction systems including regenerative braking.1996,143(6):613-615
    [86]Flinders, F.; Mathew, R.; Oghanna, W.; Energy savings through regenerative braking using retrofit converters[C]. Railroad Conference,1995., Proceedings of the 1995 IEEE/ASME Joint.1995:55-61
    [87]Min Ye; Zhifeng Bai; Binggang Cao; Robust Sliding Model Control for Regenerative Braking of Electric Vehicle[C]. Shanghai.2006,3:1-4
    [88]高道行.脉流牵引电机短路电流的计算[J].铁道学报,1987.9(6):1-8
    [89]王建冈.有源逆变系统逆变失败的分析和强迫换流保护[J].南通工学院学报:自然科学版.2003,2(4):101-103
    [90]汤钰鹏,叶斌.电力机车再生失败故障的研究[J].北方交通大学学报.1989,13(4):67-74
    [91]郭育华,连级三,张昆仑.自动过分相对电力机车的影响.机车电传动.2002(2)13-15
    [92]尹磊.电力机车自动过接触网相分段技术的研究及实现[学位论文].成都.西南交通大学.2004.3 12-17
    [93]顾翼南,王毅,王宏.电力机车自动过分相过电压分析[J].电气化铁道.2009.3:1-3.
    [94]周福林,李群湛,贺建闽.基于概率的机车过分相过电压仿真实测及其机理[J].机车电传动.2008,6:13-16
    [95]于万聚.高速电气化铁路接触网.成都.西南交通大学出版社.2003.7.163-185
    [96]宋文南,刘宝仁.电力系统谐波分析[M].北京:水利电力出版设,1995.
    [97]CEA Technology Inc.. Impact of harmonics on utility equipment: a survey and review of published work[R]. Canada:Report no. T024700-5117,2003.
    [98]谷毅,赵玉柱,张国威.关于500kV东明开关站启动调试期间发生电压谐振的分析[J].电网技术,2002,26(12):71-74.
    [99]赵树强,马燕峰,贺春.抑制谐波的配电网无功优化规划[J].电网技术,2004,28(6):71-75.
    [100]李云阁,施围.应用解析法分析中性点接地系统中的工频铁磁谐振判据和消谐措施[J].中国电机工程学报,2003,23(9):141-145.
    [101]刘凡,司马文霞,孙才新.多重分形在铁磁谐振过电压信号分析中的应用[J].电机工程学报.2006,26(18):138-141
    [102]刘凡,孙才新,司马文霞.铁磁谐振过电压混沌振荡的理论研究[J].电工技术学报.2006,21(2):103-107
    [103]王莉娜,付青.工厂供电系统谐波谐振的抑制[J].电力系统自动化,2001,25(20):41-44.
    [104]吴俊勇,肖东晖.特征结构分析及在电力系统次同步谐振研究中的应用[J].电力系统自动化,1997,21(11):1-3.
    [105]束洪春,刘娟,王超,司大军,张杰.谐振接地电网故障暂态能量自适应选线新方法[J].电力系统自动化,2006,30(11):72-76.
    [106]Smith B C, Arrillaga J, Wood A R, et al. A review of iterative harmonic analysis for AC-DC power system[J]. IEEE Trans.Power Delivery,1998,13(1):180-185
    [107]Borner A, Grebe T, Gunther E, et al. IEEE harmonics model. Simulation task force. Modeling and simulation of the propagation of harmonics in electric power networks:Part I[J] IEEE Trans. On Power Delivery,1996,11(1):452-465.
    [108]Huang Zhenyu, Xu Wilsun, Dinavahi V R. A practical harmonic resonance guideline for shunt capacitor applications[J]. IEEE Trans. On Power Delivery,2003,18(4):1382-1387.
    [109]顾伟峰,马伟明,王东,等.12/3相双绕组异步发电机自激起励时谐波谐振问题研究[J].中国电机工程学报,2004,24(6):167-171.
    [110]江振华,程时杰,傅予力,等.含有可控串联补偿电容的电力系统次同步谐振研究[J].中国电机工程学报,2000,20(6):47-52.
    [111]徐文远,张大海.基于模态分析的谐波谐振评估方法[J].中国电机工程学报,2005,25(22):89-93.
    [112]Xu Wilsun,Huang Zhengyu, Yu Cui, et al. Harmonic Resonance mode analysis[J].IEEE Transactions on Power Delevery,2005,20(2):1182-1190.
    [113]周辉,吴耀武,娄素华,熊信艮.基于模态分析和虚拟支路法的串联谐波谐振分析[J].中国电机工程学报,2007,27(28):84-89.
    [114]V.N.Vapnik.Statisticallearningtheory.NewYork:Wiley,1998-
    [115]V.N.Vapni.张学工译.统计学习理论的本质.北京:清华大学出版社,2000.
    [116]V.N.Vapnik.TheNatureofStatisticalLearningTheory.SPringer,1995
    [117]田盛丰,黄厚宽.回归型支持向量机的简化算法.软件学报,2002,13(6):1169-1172.
    [118]卢增祥,李衍达.交互支持向量机学习算法及其应用.清华大学学报:自然科学版,199,39(7):93-97.
    [119]刘江华,程君实,陈佳品.支持向量机训练算法综述信息与控制,2002,2(1):45-50
    [120]M.O.Stitson, A.Gammerman, V.VaPniK,V Vovk, C.Watkins, J.Weston in:Advances in Kernel Methods, B.Sch o Ikopf, C.J.C.Burges, And A.J.Smola Eds. Support Vector Regression with ANOVA Decomposition Kernels. MITPress.1999,285-291.
    [121]B.E.Boser, l.M.Guyon, V.N.VaPnik.ATraining Algorithm for optimal margin classifiers. Fifth Annual Workshop on Computational Learning Theory, ACM Press, Pittsburgh, PA,1992, 144-152.
    [122]E.osuna, R.Freund, F.Girosi. An Improved Training Algorithm for Support Vector Machines, ICNNSP97, NewyorK.1997,276-285.
    [123]E.osuna, R.Freund, F.Girosi. Support vector machines: training and Application. Cambridge, MA:Massachusetts Institute of Technology, AILab,1997.
    [124]TJoachims. Making large scale support vector machines learning practical, Sch61kopf B, BurgesC, SmolaA.J, Advances in kernel methods-support vector learning, Cambridge, MA:MIT Press,1999,169-184.
    [125]J.plat. Fast training of support vector machines using sequential minimalop-timization· B.ScholkoPf, C.Burges, A.Smola. Advances in Kernel Methods-Support Vector Learning. Cambridge, MA:MIT Press,1999,185-208.
    [126]J.A.K.Suykens, T.V.Gestel, etal. A Support Vector Machine formulation to PCA Analysis and its Kernel version. ESAT-SCD-SISTA Technical Report 2002-68, Beigium: Katholieke Universiteit Leuven,2002.
    [127]H.GKrebel, Ulrich. Pairwise classification and support vector machines. In:Scholkopf Bernhard(edi.). Advances in Kernel Methods: Support Vector Learning. Massachusetts, The MIT Press,1999,255-268.
    [128]罗泽举.支持向量机学习算法及应用研究中的若干问题.博士学位论文.中山大学.2006:2-3
    [129]V.N.Vapnik.许建华,张学工译.统计学习理论.北京:电子工业出版社,2004.
    [130]N.Cristianini, J.Shawe-Taylor.支持向量机导论.李国正,王猛,曾华军译.北京:电子工业出版社,2005.
    [131]郭辉,刘贺平,王玲.最小二乘支持向量机参数选择方法及其应用研究[J].系统仿真学报.2006,18,7:2033-2051
    [132]Wald. A. Statistical Decision Functions. New York: Wiley,1950
    [133]H. Robbins. An empirical Bayes approach to statistics. In Proc Third Berkeley Symposium on Math. Statist. And Prob.,1955,157-164
    [134]H.Robbins. The empirical bayes approach to statistical decision problems. Ann. Math,1964
    [135]D.Heckerman. A Tutorial on Learning with Bayesian Networks. Technical Report, MSR TR-95-06,1995
    [136]P. Lucas. Expert Knowledge and its Role in Learning Bayesian Networks in Medicine:an Appraisal. http://citeseer.nj.net.com/465175.html
    [137]Friedman,D.Geiger. Moises Goldszmidt. Bayesian Network Classifiers. http://citeseer.nj.n et.com/127856.html
    [138]D.He ckerman,G.Meek. Models and selection Criteria for Regression and Classification. Technical Report,MSR-TR-97-08.http://citeseer.nj.net.com/heckerman97model.html.
    [139]Scholkspf B, Smola A, Mtiller K M. Nonlinear component analysis as a kernel eighenvalue problem[J]. Neural Computation,1998,10:1299-1319.
    [140]Press W H, Teukolsky S A. Vetterling W T, Flannery B P. Numerical Recipes in C.2nd ed. New York:Cambridge University Press,1992
    [141]朱龙驹.韶山4型电力机车(下)[M].北京:中国铁道出版社.
    [142]谢步明.韶山7型电力机车[M].北京:中国铁道出版社.
    [143]列车牵引计算规程解释[M].北京:中国铁道出版社.
    [144]顾松彬.用传递函数仿真SS3型电力机车的牵引起动过程[J].华东交通大学学报.1998,15(3):38-44
    [145]宋雷鸣.动车组传动与控制[M].北京:中国铁道出版社.
    [146]朱希荣,周晓锋,周渊深.NPC三电平逆变器SVPWM算法的研究及仿真实现[J].淮海工学院学报(自然科学版).2007,16(1):27-30
    [147]田玉超,刘勇,丛望.SVPWM算法控制三电平逆变器仿真[J].应用科技.2005,32(2):37-39
    [148]张伯泽,何礼高,陈鑫兵.基于三电平逆变器的异步电机矢量控制研究[J].电气传动自动化.2006,28(6):15-18
    [149]陈亚爱,李政学,李正熙等.三电平逆变器空间电压矢量控制算法仿真研究[J].北方工业大学学报.2007,19(1):28-32
    [150]李文平,张建成.韶山4型电力机车两位置转换开关故障分析.机车电传动.2005.1
    [151]李时璋,刘应军.两位置转换开关故障处理及其改进的探讨.机车电传动.2001.6
    [152]A. D. RAJKUMAR, R. SOMANATHAM.. Determination of DC Motor Transients Using a Microcomputer[J]. IEEE Transactions on Instrumentation and Measurement.1998 31(4):600-604
    [153]Orlando Silvio Lobosco. Modeling and Simulation of DC Motors in Dynamic Conditions Allowing for the Armature Reaction[J]. IEEE Transactions on Energy Conversion,1999. 14(4):1288-1293
    [154]El-Arabawy I.F, Yousef H.A, Mostafa, M.Z, et al. Parameters Estimation of Nonlinear Model of DC Motors Using Neural Network[C].26th Annual Conference of the IEEE. Nagoya, Japan. 2003,vol.3:1997-2000.
    [155]Avitan I, Skormin V. Mathematical Modeling and Computer Simulation of Separately Excited dc Motor with Independent Armature/field Control[J]. Industrial Electronics, IEEE Transactions on Publication.1990,37(6):83-489
    [156]Lobosco, O.S. Modeling and simulation of DC motors in dynamic conditions[C]. Electric Machines and Drives Conference. Milwaukee, WI, USA.1997,MB2/1.1-MB2/1.3
    [157]Cezayirli A, Ciliz K. Multiple Model Based Adaptive Control of a DC Motor under Load Changes[C]. Proceedings of the IEEE International Conference on Mechatronics,2004. ICM '04.2004,328-333.
    [158]冯慈璋,马西奎.工程电磁场导论.第1版.北京.高等教育出版社.2000,6.46--49
    [159]王兆安,黄俊.电力电子技术.第4版.北京.机械工业出版社.2000.79-90
    [160]马开国,杜庆萱,连级三.电力机车概论.第1版.北京.中国铁道出版社.1990,9.1-13,34-63,68-97
    [161]王洪杰,高月华,徐广健.8K电力机车上加装隔离开关误动作运行装置.机车电传动.1999.5
    [162]Bellman R. Introduction to matrix analysis[M].Second Edition, New York:McGraw-Hill Inc,1970.
    [163]Perez-Arriaga I J, Verghese G C, Schweppe F C. Selective modal analysis with applications to electric power systems Ⅰ. heuristic introduction[J].IEEE Trans.on PAS, 1982,101(9):3117-3125.
    [164]Pintelon, R.;Peeters, B.; Guillaume, P.; Continuous-Time Operational Modal Analysis in the Presence of Harmonic Disturbances[C]. Instrumentation and Measurement Technology Conference Proceedings,2008. IMTC 2008. IEEE. Victoria, BC.2008:326-331
    [165]Varricchio, S.L.; Gomes, S., Jr.; Martins, N.; Modal analysis of industrial system harmonics using the s-domain approach[J]. Power Delivery, IEEE Transactions on.2004,19(3):1232-1237
    [166]Hiyama, T.; Suzuki, N.; Real time modal analysis of power system oscillations[C]. Circuits and Systems,2000. Proceedings. ISCAS 2000 Geneva. The 2000 IEEE International Symposium on. Geneva.2000:225-228
    [167]George J. Wakileh.徐政译.电力系统谐波—基本原理、分析方法和滤波器设计[M].北 京:机械工业出版社.
    [168]张贤达.矩阵分析与应用.北京:清华大学出版社
    [169]Jacobson D A N Examples of ferroresonane in a high voltage power system [C]. IEEE Power Engineering Society Ge neral Meting,2003:1206-1212.
    [170]Zia Emin, Bashar A t. Al Zahawi. Quantification of thechaotic behavior of ferroresonant voltage transformer circuits. IEEE Trans on Fundamental Theory and Applications,2001, 48(6):757-759
    [171]A Ben Tal, Shein D. Study ferroresonance in actual power systems by bifurcation iagram. Electric Power Systems Research,1999, (49):175-183
    [172]Marti J R. Soudack A C Ferroresonance in power systems:fundamental solutions[J]. IEE Proeedings-Generation. Transmission and Distribution.1991.138(4):321-329.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700