An Optimal Design of Large Scale Permanent Magnet Pole Shape Using Adaptive Response Surface Method With Latin Hypercube Sampling Strategy

Abstract

The Latin hypercube sampling (LHS) strategy is applied to the optimization of a magnet pole shape of large scale BLDC motor to minimize the cogging torque with an adaptive response surface method. In the strategy, the qualities of sampling points are evaluated in the viewpoint of uniform space-filling by using Min-distance and Max-distance metrics, and optimum sampling points are obtained in Pareto-optimal sense. An adaptive sampling point insertion is also achieved utilizing design sensitivities computed by using finite element method to get a reasonable response surface with a relatively small number of sampling points. The LHS strategy is incorporated with (I + lambda) evolution strategy, and applied to the permanent magnet pole shape optimization of 6 MW BLDC motor, and the results are compared with those of other algorithms.