Application of Surrogate Optimization Routine with Clustering Technique for Optimal Design of an Induction Motor

Energies

Md Masum Billah¹  Anouar Belahcen¹  Aswin Balasubramanian¹  Floran Martin¹  Osaruyi Osemwinyen¹ 

[1] Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland;

关键词: induction motors;    surrogate optimization;    Box–Behnken design;    Latin-hypercube sampling;    clustering;    particle swarm optimization;   

DOI  :  10.3390/en14165042

来源: DOAJ

【 摘 要 】

This paper proposes a new surrogate optimization routine for optimal design of a direct on line (DOL) squirrel cage induction motor. The geometry of the motor is optimized to maximize its electromagnetic efficiency while respecting the constraints, such as output power and power factor. The routine uses the methodologies of Latin-hypercube sampling, a clustering technique and a Box–Behnken design for improving the accuracy of the surrogate model while efficiently utilizing the computational resources. The global search-based particle swarm optimization (PSO) algorithm is used for optimizing the surrogate model and the pattern search algorithm is used for fine-tuning the surrogate optimal solution. The proposed surrogate optimization routine achieved an optimal design with an electromagnetic efficiency of $93.90\%$, for a 7.5 kW motor. To benchmark the performance of the surrogate optimization routine, a comparative analysis was carried out with a direct optimization routine that uses a finite element method (FEM)-based machine model as a cost function.

【 授权许可】

Unknown