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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Mohd Saufi Ahmad
Univeristi Malaysia Perlis
Control & Systems Engineering Electrical & Electronics Engineering

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Optimization of double stator PMSM with different slot number in inner and outer stators using genetic algorithm Mohd Saufi Ahmad; Dahaman Ishak; Tiang Tow Leong; Mohd Rezal Mohamed
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 12, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v12.i2.pp726-735

Abstract

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.
Co-Authors Dahaman Ishak Mohd Rezal Mohamed Tiang Tow Leong