Optimal design of a switched reluctance generator for small wind power system using a genetic algorithm

This paper deals with the optimal design of a 1 kW-switched reluctance generator (SRG) for wind power applications. The optimal design of the SRG uses the design variables based on the basic design model. Latin hypercube sampling (LHS) is used to extract the samples of design variables. Kriging Method is used to approximate the objective and constraints functions, while genetic algorithm (GA) is used to optimize the generator design. The efficiency and the power density of the basic design model and the optimal model are compared.