<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper discusses the optimization of surface-mounted permanent magnet brushless AC (PMBLAC) motor using Analytical Sub-domain model with Differential Evolution Algorithm (ASDEA). Only two regions were considered in this analytical sub-domain model, <italic>ie</italic> magnet and airgap regions, with assistance of Complex Relative Permeance Function (CRPF) to account for the stator slotting effect. Five machine parameters were chosen to be optimized, namely the magnet arc-pole-pitch ratio, slot opening width, magnet thickness, airgap length and stator inner radius. The optimization process has four objectives, <italic>ie</italic> minimum torque ripple, low cogging torque, high efficiency, and high output torque. The results from the optimized ASDEA were compared with the Analytical Sub-domain Genetic Algorithm (ASGA) and further validated against 2-D finite element model (FEM). Results show a good agreement between analytically optimized models and finite element model. The ASDEA has faster computational time compared to ASGA, and this provides benefit in terms of reducing the machine design parameterization time and less redundancy work required to achieve motor design specifications.</p></abstract>

This paper discusses the optimization of surface-mounted permanent magnet brushless AC (PMBLAC) motor using Analytical Sub-domain model with Differential Evolution Algorithm (ASDEA). Only two regions were considered in this analytical sub-domain model, ie magnet and airgap regions, with assistance of Complex Relative Permeance Function (CRPF) to account for the stator slotting effect. Five machine parameters were chosen to be optimized, namely the magnet arc-pole-pitch ratio, slot opening width, magnet thickness, airgap length and stator inner radius. The optimization process has four objectives, ie minimum torque ripple, low cogging torque, high efficiency, and high output torque. The results from the optimized ASDEA were compared with the Analytical Sub-domain Genetic Algorithm (ASGA) and further validated against 2-D finite element model (FEM). Results show a good agreement between analytically optimized models and finite element model. The ASDEA has faster computational time compared to ASGA, and this provides benefit in terms of reducing the machine design parameterization time and less redundancy work required to achieve motor design specifications.

Optimization of surface-mounted permanent magnet brushless AC motor using analytical model and differential evolution algorithm

School of Electrical and Electronic Engineering, Engineering Campus

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

This paper discusses the optimization of surface-mounted permanent magnet brushless AC (PMBLAC) motor using Analytical Sub-domain model with Differential...

Optimization of surface-mounted permanent magnet brushless AC motor using analytical model and differential evolution algorithm

Publié en ligne: 18 juil. 2019

Pages: 208 - 217

Reçu: 17 avr. 2019

DOI: https://doi.org/10.2478/jee-2019-0029

Mots clés
analytical sub-domain, differential evolution, permanent magnet, brushless motor

© 2019 Mohd Rezal Mohamed et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Optimization of surface-mounted permanent magnet brushless AC motor using analytical model and differential evolution algorithm

Publié en ligne: 18 juil. 2019

Pages: 208 - 217

Reçu: 17 avr. 2019

DOI: https://doi.org/10.2478/jee-2019-0029

Mots clésanalytical sub-domain, differential evolution, permanent magnet, brushless motor

© 2019 Mohd Rezal Mohamed et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
analytical sub-domain, differential evolution, permanent magnet, brushless motor