Optimal design of direct-driven PM wind generator using adaptive univariate dynamic encoding algorithm for searches (uDEAS)

In this paper, optimal design of the direct-driven PM wind generator, combined with uDEAS (univariate Dynamic Encoding Algorithm for Searches) and FEA (Finite Element Analysis), has been performed to maximize the Annual Energy Production (AEP) over the whole operating wind speed. In particular, an adaptive scheme of arranging search variable sequence in uDEAS is newly proposed according to variable's measured gradient to cost function. The proposed adaptive scheme is embedded in uDEAS and validated through two test functions, and the modified uDEAS is applied to optimal design of the direct-driven PM wind generator. With the comparable quality of the attained solution, uDEAS enormously reduces computation time when compared with Genetic Algorithm (GA) implemented by the parallel computing method.