LAPSE:2023.33978
Published Article
LAPSE:2023.33978
Application of Surrogate Optimization Routine with Clustering Technique for Optimal Design of an Induction Motor
April 24, 2023
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.
Record ID
Keywords
Box–Behnken design, clustering, induction motors, Latin-hypercube sampling, Particle Swarm Optimization, pattern search, surrogate optimization
Subject
Suggested Citation
Balasubramanian A, Martin F, Billah MM, Osemwinyen O, Belahcen A. Application of Surrogate Optimization Routine with Clustering Technique for Optimal Design of an Induction Motor. (2023). LAPSE:2023.33978
Author Affiliations
Balasubramanian A: Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland
Martin F: Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland
Billah MM: Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland
Osemwinyen O: Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland [ORCID]
Belahcen A: Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland [ORCID]
Martin F: Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland
Billah MM: Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland
Osemwinyen O: Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland [ORCID]
Belahcen A: Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland [ORCID]
Journal Name
Energies
Volume
14
Issue
16
First Page
5042
Year
2021
Publication Date
2021-08-17
Published Version
ISSN
1996-1073
Version Comments
Original Submission
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PII: en14165042, Publication Type: Journal Article
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LAPSE:2023.33978
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doi:10.3390/en14165042
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[v1] (Original Submission)
Apr 24, 2023
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Apr 24, 2023
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