Open AccessAn Adaptive Optimization Algorithm Based on Kriging Interpolation with Spherical Model and its Application to Optimal Design of Switched Reluctance Motor
Author(s) -
Bin Xia,
Ziyan Ren,
Yanli Zhang,
Chang Seop Koh
Publication year - 2014
Publication title -
journal of electrical engineering and technology/journal of electrical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.226
H-Index - 27
eISSN - 2093-7423
pISSN - 1975-0102
DOI - 10.5370/jeet.2014.9.5.1544
Subject(s) - kriging , control theory (sociology) , switched reluctance motor , surrogate model , rotor (electric) , torque ripple , optimal design , interpolation (computer graphics) , adaptive sampling , stator , genetic algorithm , computer science , sampling (signal processing) , torque , mathematical optimization , algorithm , mathematics , engineering , induction motor , direct torque control , artificial intelligence , machine learning , motion (physics) , control (management) , thermodynamics , physics , filter (signal processing) , voltage , monte carlo method , computer vision , mechanical engineering , statistics , electrical engineering
In this paper, an adaptive optimization strategy utilizing Kriging model and genetic algorithm is proposed for the optimal design of electromagnetic devices. The ordinary Kriging assisted by the spherical covariance model is used to construct surrogate models. In order to improve the computational efficiency, the adaptive uniform sampling strategy is applied to generate sampling points in design space. Through several iterations and gradual refinement process, the global optimal point can be found by genetic algorithm. The proposed algorithm is validated by application to the optimal design of a switched reluctance motor, where the stator pole face and shape of pole shoe attached to the lateral face of the rotor pole are optimized to reduce the torque ripple.