Modeling and sizing by optimization of a Brushless Doubly-Fed Reluctance Machine

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 sizing equations iteratively by maximizing or minimizing an objective function. A Pareto Front strategy is used to illustrate the models capabilities, highlighting the trade-offs on the design of this kind of electrical machines. The optimization model provides fast results to define a first 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.