Optimal design of direct-driven PM wind generator using memetic algorithm coupled with FEM

This paper presents the novel implementation of memetic algorithm with GA (genetic algorithm) and MADS (mesh adaptive direct search), which is applied for optimal design methodology of electric machine. This hybrid algorithm has been developed for obtaining the global optimum rapidly, which is effective for optimal design of electric machine with many local optima and much longer computation time. As a heuristic search algorithm, MADS combined with GA is validated with the rastrigin function and the shubert function with distinguished multimodal characteristics by investigating the evaluation number for optima convergence. In particular, the proposed memetic algorithm has been forwarded to optimal design of direct-driven PM wind generator for maximizing the annual energy production (AEP), of which design objective should be obtained by FEA (finite element analysis). After all, it is shown that MADS combined with GA has contributed to reducing the computation time effectively for optimal design of PM wind generator when compared with purposely developed GA implemented with the parallel computing method.