Electrical machine optimization using a kriging predictor

Author

Duchaud, J.-L. ; Hlioui, S. ; Louf, F. ; Gabsi, Mohamed

Author_Institution

SATIE, ENS Cachan, Cachan, France

fYear

2014

fDate

22-25 Oct. 2014

Firstpage

3476

Lastpage

3481

Abstract

This paper presents an optimization on a surface response created by kriging. It focuses on the predictor creation, its refinement and on the advantage of such a method over a direct optimization. In order to reduce the number of evaluations needed to construct the predictor, an adaptive method for the trial site selection is introduced as an improvement for the Latin Hypercubic Sample (LHS) algorithm. The method is applied to the optimization of a flux switching synchronous machine and the results are illustrated for two and four parameters.

Keywords

interpolation; optimisation; regression analysis; sampling methods; synchronous machines; LHS algorithm; computation time reduction; direct optimization; electrical machine optimization; flux switching synchronous machine optimization; kriging predictor; latin hypercubic sample algorithm; surface response optimization; trial site selection; Computational modeling; Correlation; Optimization; Prediction algorithms; Predictive models; Switches; Torque; Adaptive kriging; computation time reduction; electrical machine; metamodel; optimization; surrogate model;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical Machines and Systems (ICEMS), 2014 17th International Conference on

Conference_Location

Hangzhou

Type

conf

DOI

10.1109/ICEMS.2014.7014091

Filename

7014091