Title :
A Novel BVC-RBF Neural Network Based System Simulation Model for Switched Reluctance Motor
Author :
Cai, J. ; Deng, Z.Q. ; Qi, R.Y. ; Liu, Z.Y. ; Cai, Y.H.
Author_Institution :
Coll. of Autom. & Eng., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China
fDate :
4/1/2011 12:00:00 AM
Abstract :
Developing a precise system simulation model is a critical step in the design and analysis of optimal control strategies for a switched reluctance motor (SRM). To achieve this objective, the following works have been done in this paper. 1) A 3-D FEA model based on double scalar magnetic potential method (DSMP) is developed for obtaining the distributions of SRM magnetic field, then the flux linkage characteristics are calculated by using enhanced incremental energy method (EIEM). 2) In order to enhance modeling accuracy of the nonlinear flux linkage, a new RBF neural network with boundary value constraints (BVC-RBF) is used for approximating, based on the calculated flux linkage data. 3) The nonlinear BVC-RBF based simulation model of the SRM system is established for dynamic analysis with the power system block (PSB) modules of Matlab/simulink. 4) Simulation and experimental results are presented and compared for model validation. The validation study indicates that the developed model is highly accurate.
Keywords :
finite element analysis; mathematics computing; optimal control; power engineering computing; power system harmonics; radial basis function networks; reluctance motors; 3D FEA model; BVC-RBF neural network; DSMP; EIEM; Matlab-Simulink; PSB module; SRM magnetic field; double scalar magnetic potential method; dynamic analysis; enhanced incremental energy method; flux linkage characteristics; optimal control strategy; power system block module; switched reluctance motor; system simulation model; FEA; RBF neural network; modeling; simulation; switched reluctance motor;
Journal_Title :
Magnetics, IEEE Transactions on
DOI :
10.1109/TMAG.2011.2105273
