Optimal Design of Winding Transposition of Power Transformer Using Adaptive Co-Kriging Surrogate Model
Bin Xia, Seokyeon Hong, Kyung Kook Choi, Chang Seop Koh · IEEE Transactions on Magnetics · 2017
An adaptive co-Kriging surrogate model, which is numerically more efficient and accurate than a conventional co-Kriging model, is developed, and incorporated into a heuristic optimization algorithm to be applied to the optimal design of transposition of power transformer windings. The sampling data of the proposed adaptive co-Kriging consist of a few expensive and many cheap data to save the computational efforts while increasing modeling accuracy. A criterion on the minimum number of expensive sampling data is investigated to achieve a desired fitting accuracy.