Abstract:
The theme dealt with in this thesis focuses on the modeling of a variable reluctance
motor (VRM) adapted to the process of optimizing its performance we have developed and
tested. Knowing that the performance of VRM depends as well on the geometric structure, the nonlinear characteristics of the materials used and the converter control parameters, we therefore paid particular attention to the associated mathematical model, based on an
analytical approach taking into account the peculiarity of the VRM run in saturated state.
Despite the accuracy of numerical approaches such as finite element method,
researchers are moving increasingly towards hybrid methods combining their analytical
models as better suited to an optimization process; this is the approach we have adopted here.
Thus we have oriented our work towards finding hybrid models (analytical- numerical)
sufficiently accurate while being rapid execution. The validation of these models was made
by comparing the results obtained and the numerical simulations by finite elements in Flux-
2D.
Validation and exploitation of results of different simulations allowed the continuation
of work to optimize the geometric parameters for stator and rotor teeth of the studied
prototype. After state of the art different algorithms of optimizations present in the literature,
our choice fell on Genetic Algorithms and Swarm particles. The various simulations in
MATLAB environment allowed get optimized dental angles and significantly improve the
average couple.
