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

Author

Listed:
  • Aswin Balasubramanian

    (Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland)

  • Floran Martin

    (Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland)

  • Md Masum Billah

    (Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland)

  • Osaruyi Osemwinyen

    (Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland)

  • Anouar Belahcen

    (Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland)

Abstract

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.

Suggested Citation

  • Aswin Balasubramanian & Floran Martin & Md Masum Billah & Osaruyi Osemwinyen & Anouar Belahcen, 2021. "Application of Surrogate Optimization Routine with Clustering Technique for Optimal Design of an Induction Motor," Energies, MDPI, vol. 14(16), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5042-:d:615956
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    References listed on IDEAS

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    Cited by:

    1. Anouar Belahcen & Armando Pires & Vitor Fernão Pires, 2023. "Magnetic Material Modelling of Electrical Machines," Energies, MDPI, vol. 16(2), pages 1-3, January.
    2. Maria Dems & Krzysztof Komeza & Jacek Szulakowski & Witold Kubiak, 2022. "Increase the Efficiency of an Induction Motor Feed from Inverter for Low Frequencies by Combining Design and Control Improvements," Energies, MDPI, vol. 15(2), pages 1-17, January.
    3. Marcel Torrent & Balduí Blanqué, 2021. "Influence of Equivalent Circuit Resistances on Operating Parameters on Three-Phase Induction Motors with Powers up to 50 kW," Energies, MDPI, vol. 14(21), pages 1-22, November.

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