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爪极发电机建模及参数优化设计 被引量:15

Modeling and Parameters Optimal Design of Claw-pole Alternator
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摘要 建立了爪极发电机的三维有限元模型,对其漏磁进行了深入的分析。通过电机参数样本空间设计,利用支持向量机对爪极发电机的漏磁系数进行非线性回归建模分析,然后基于混沌理论对爪极发电机结构参数进行优化。仿真结果表明,支持向量机用于爪极发电机非参数建模准确可行,并且是高效的,非常适合于需要大规模迭代计算的参数优化。将有限元电磁仿真与支持向量机结合用于非参数建模,以及在非参数模型的基础上用混沌进行优化,为爪极发电机及其它的电磁工程设计提供了一种新的思路。 Three dimension finite element model (3D-FEM) of the claw-pole alternator is presented in this paper, and the leakage is analysed in detial. By designing the sample space of the parameters, nonlinear regression modeling of the claw-pole alternator based on support vector machines (SVM) is introduced. Parameters optimization of the claw-pole al{ernator is also introduced which is based on chaos. Simulation results prove that the nonparametric modeling based on SVM is feasible and high efficiency, and it is very fit for the parameters optimization which needs large-scale iterative calculating. Using the integration of FEM and SVM to nonparametric modeling, and parameter optimization based on it provide a novel way for the optimum design of the claw-pole alternator and other engineering.
机构地区 合肥工业大学
出处 《中国电机工程学报》 EI CSCD 北大核心 2006年第21期138-142,共5页 Proceedings of the CSEE
基金 国家自然科学基金项目(50077005)~~
关键词 爪极发电机 三维有限元 支持向量机 漏磁系数 混沌 claw-pole alternator three dimension finite element model support vector machines leakage coefficient chaos
作者简介 鲍晓华(1972-),男,湖北麻城人,博士研究生,研究方向为汽车发电机优化设计,xiaohua1972@163.com 王群京(1960-),男,安徽蚌埠人,教授,博士生导师,研究方向为特种电机及其控制.
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参考文献15

  • 1Ramesohl I,Henneberger G,Kuppers S,et al.Three dimensional calculation of magnetic forces and displacements of a claw-pole generator[J].IEEE Transactions on Magnetics,1996,32(3):1685-1688.
  • 2Vapnik V N.An overview of statistical learning theory[J].IEEE Trans.Neural Networks,1999,10(5):988-999.
  • 3Vapnik V N.The nature of statistical learning theory[M].New York:Springer,1995.
  • 4唐巍,李殿璞.电力系统经济负荷分配的混沌优化方法[J].中国电机工程学报,2000,20(10):36-40. 被引量:134
  • 5Wang D,Han P,Ren Q.Chaos optimization variable arguments PID controller and its application to main steam pressure regulating system[C].The First International Conference on Machine Learning and Cybernetics,Beijing,2002.
  • 6王群京,倪有源,姜卫东,张学.汽车用爪极发电机负载磁场和电感的分析与计算[J].中国电机工程学报,2004,24(3):91-95. 被引量:21
  • 7Demerdash N A,Nehl T W,Fouad F A.Finite element formulation and analysis of three dimensional magnetic field problems[J].IEEE Transactions on Magnetics,1980,16(2):1092-1994.
  • 8Demerdash N A,Nehl T W,Fouad F A,et al.Three dimensional finite element vector potential formulation of magnetic fields in electrical apparatus[J].IEEE Transactions on Power Apparatus and Systems,1981,100(10):4104-4111.
  • 9王群京,马飞,李国丽,陈军.爪极电机空载时三维磁场的数值分析和电感计算[J].中国电机工程学报,2002,22(1):38-42. 被引量:29
  • 10Demerdash N A,Wang R,Seunde R R.Three dimensional magnetic fields in extra high speed modified lundell alternators computed by combined vector-scalar magnetic potential finite element method[J].IEEE Transactions on Energy Conversion,1992,7(2):353-366.

二级参考文献26

  • 1Liu K. Comparison of very short-term load forecasting technique[J]. IEEE Trans. Power Systems, 1996,11(2): 877-882.
  • 2Hippert H S, Pefreira C E, Souza R C. Neural network for short-term load forecasting: A review and evaluation[J].IEEE Trans. Power System. 2001,16(2): 44-54.
  • 3Muller K R, Smola A J, Ratsch G, et al.Prediction time series with support vector machines[C].In Proc of ICANN'97., Springer LNCS 1327, Bedin,1997, 999-1004.
  • 4Papadakis S E, Theocharis J B, Kiartzis S J, et al. A novel approach to short-term load forecasting using fuzzy neural net-works[J].IEEE Trans. Power Systems, 1998,13(2):480-492.
  • 5Vapnik V, Golowich S, Smola A. Support vector method for function approximation, regression estimation, and signal processing[M].Cambridge, MA, MIT Press, 1997, 281-287.
  • 6Smola A J. Regression estimation with support vector learning machines[D]. Technische Universit"at M" unchen.1996.
  • 7Vapnik V N. The nature of statistical learning theory[M]. New York:Springer, 1995.
  • 8Mukherjee S, Osuna E, Girosi F. Nonlinear prediction of chaotic time series using support vector machines[C]. Proceedings of NNSP '97,Amelia Island,FL,1997.
  • 9Smola A J, Scholkopf B. A tutorial on support vector regression[R].NeuroCOLT Tech. Rep.TR 1998-030,Royal Holloway College,London, U.K,1998.
  • 10Shevade S K, Keerthi S S, Bhattcharyy C, et al. Improvements to SMO algorithm for SVM regression[J]. IEEE Trans. on Neural Network, 2000,11(5): 1188-1193.

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