Multiobjective design of permanent magnet synchronous machines based on analytical sub-domain particle swarm optimization

Rezal, M.; Ishak, Dahaman; Salah, Wael A.

doi:10.1109/cencon.2017.8262489

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…magnet region and airgap region. The magnetic flux density in the airgap is obtained analytically using ( 7) and (8) for its radial and tangential components respectively [20]…”

Section: Analytical Sub-domain Modelmentioning

confidence: 99%

Performance evaluation of multi-phase permanent magnet synchronous motor based on different winding configurations and magnetization patterns

Rezal¹,

Ishak²

2019

IJPEDS

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Permanent magnet synchronous motor (PMSM) is the most reliable and efficient machine that widely used in robotics and automation, industrial applications, electric vehicles, home appliances, aircraft and aerospace technology due to its high efficiency, good dynamic performance and high torque density. In this paper, the influence of various types of winding configuration and different magnetization patterns in the performance of a five-phase PMSM is investigated. Three types of magnetization patterns such as radial magnetization (RM), parallel magnetization (PaM), and multi-segmented Halbach magnetization (SH) are applied to the five-phase 10-slot/4-pole PMSM during open-circuit and on-load conditions. A 2D finite element method (FEM) is intensively used in this investigation to model and predict the electromagnetic characteristics and performance of the PMSM. The detailed results from the finite-element analysis (FEA) on the cogging torque, induced back-emf, airgap flux density and electromagnetic torque are analysed. The induced back-emf of the machine is computed further into its harmonic distortions. Additionally, the skewing method for minimization of cogging torque of PMSM is proposed. From the results, it is observed that the five-phase, 10-slot/4-pole PMSM with double layer distributed winding and parallel magnetization gives the best machine performance.

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“…magnet region and airgap region. The magnetic flux density in the airgap is obtained analytically using ( 7) and (8) for its radial and tangential components respectively [20]…”

Section: Analytical Sub-domain Modelmentioning

confidence: 99%

Performance evaluation of multi-phase permanent magnet synchronous motor based on different winding configurations and magnetization patterns

Rezal¹,

Ishak²

2019

IJPEDS

Self Cite

View full text Add to dashboard Cite

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“…It can contribute to motor noise and vibration; therefore, it should be minimized as much as possible. The phase back-emf can be obtained during the open-circuit condition using (7) [25]. While, the cogging torque is determined using (8).…”

Section: Analytical Sub-domain Modellingmentioning

confidence: 99%

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

Mohamed

Ishak

2019

Journal of Electrical Engineering

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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.

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“…To evaluate the performances of DS-PMSM, numerical methods such as the finite element analysis (FEA) have been intensively used for designing and determining the optimal configuration of PMSMs before proceeding to fabrication and manufacture. However, manually varying the important parameters in the machine constructions will require a longer computational time and impractical to achieve the best motor performance [17]- [18].…”

Section: Introductionmentioning

confidence: 99%

Optimization of double stator PMSM with different slot number in inner and outer stators using genetic algorithm

Ahmad

Ishak

Leong

et al. 2021

IJPEDS

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<span lang="EN-US">This paper describes the performance enhancement of double stator permanent magnet synchronous machines (DS-PMSM) based on genetic algorithm optimization (GAO). Generally, throughout the development stage, an analytical calculation is implemented to build the initial model of the DS-PMSM since the analytical calculation can provide the initial parameters based on the types and materials used in the machine design. For further improvement, GAO might potentially be applied to provide the optimization technique in searching the optimal motor parameters iteratively and intelligently with specific objective functions. For this aim, a three-phase, DS-PMSM with different number of slots between the outer and inner stators is first designed by using analytical parameter estimation and then later optimized by GAO. The outer and inner stators have 12-slots and 9-slots respectively, while, the rotor carries 10 magnetic poles. Four main input motor parameters, i.e. outer stator slot opening, outer magnet pole arc, inner stator slot opening and inner magnet pole arc are varied and optimized to achieve the design objective functions, i.e. high output torque, low torque ripple, low cogging torque and low total harmonic distortion (THDv). The results from the optimized GAO are compared with the initial motor model and further validated by finite element method (FEM). The results show a good agreement between GAO and FEM. GAO has achieved very significant improvements in enhancing the machine performance.</span>

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Multiobjective design of permanent magnet synchronous machines based on analytical sub-domain particle swarm optimization

Cited by 9 publications

References 21 publications

Performance evaluation of multi-phase permanent magnet synchronous motor based on different winding configurations and magnetization patterns

Performance evaluation of multi-phase permanent magnet synchronous motor based on different winding configurations and magnetization patterns

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

Optimization of double stator PMSM with different slot number in inner and outer stators using genetic algorithm

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