MODELING AND SIZING BY OPTIMIZATION OF A BRUSHLESS DOUBLY-FED RELUCTANCE MACHINE
Résumé
The paper proposes an optimization approach to address the modeling and sizing complexities of Brushless Doubly-Fed Reluctances Machines. A semi-analytical model and a reluctance network model are coupled to a deterministic optimization algorithm where several inputs and outputs parameters can be constrained to solve system equations iteratively by maximizing or minimizing an objective function. A Pareto Front strategy is used to illustrate the model capabilities, highlighting the trade-offs on the design of this kind of electrical machines. The optimization model provides fast results to define an initial design, reducing the number of unknown parameters of early development stages. This saves computation time and computer resources in the design process compared to Finite Element Analysis.