Switched reluctance motor design using neural-network method with static finite-element simulation Academic Article

abstract

• The paper describes a neural network method for optimal design of a switched reluctance motor (SRM). The approach maximizes average torque while minimizing torque ripple, considering mainly the stator and rotor geometry parameters. Before optimization takes place, an experimental validation of the SRM model, based on the finite-element method, is performed. The validation predicts average torque and torque ripple characteristics for several motor configurations while stator and rotor pole arcs are varied. The numerical results are highly nonlinear, and a function approximation of the data is therefore difficult to implement. We therefore interpolate the data by using a neural network based on a generalized radial basis function. The computed results allow us to search for optimum motor parameters. The optimum design was confirmed by numerical field solutions. 2007 IEEE.

authors

• Toliyat, Hamid

published proceedings

• IEEE TRANSACTIONS ON MAGNETICS

author list (cited authors)

• Sahraoui, H., Zeroug, H., & Toliyat, H. A.

citation count

• 38

complete list of authors

• Sahraoui, H||Zeroug, H||Toliyat, HA

publication date

• December 2007

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Transactions on Magnetics  Journal

keywords

• Design
• Finite-element Method
• Modeling
• Neural-network Modeling
• Optimization
• Simulation
• Srm Drives

Digital Object Identifier (DOI)

• 10.1109/TMAG.2007.907990

start page

• 4089

end page

• 4095

volume

• 43

issue

• 12

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Ftmag.2007.907990