Experimental Optimal Design of Slotless Brushless PM Machines Based on 2-D Analytical Model


This paper presents a good optimal style procedure for brushless permanent magnet (BLPM) machines based on an adaptive metaheuristic optimization technique. two-D analytical expressions for the calculation of the magnetic flux density, electromagnetic torque, back electromotive force, and self-inductance and mutual-inductance are used to optimally style a slotless brushless motor with surface-mounted magnets. The proposed approach combines the computational accuracy of the two-D analysis and therefore the computational speed thanks to the analytical expressions. The objective functions are the ability losses and the motor volume to be simultaneously minimized subject to 3 constraints: one) the specified electromagnetic torque; 2) the desired maximum rotational velocity; and 3) the limit of the stator core flux density. The optimization drawback is solved using a fuzzy adaptive particle swarm optimization (FAPSO) technique. To evaluate the efficacy and effectiveness of the FAPSO technique, its results are compared with those of the traditional PSO and genetic algorithms. To research the influence of the armature current waveforms on the planning results, 3 different optimization problems with completely different armature current waveforms (ideal rectangular, six-step, and sinusoidal), but identical objectives, constraints, and optimization variables are outlined and solved. Finally, a BLPM machine has been designed and made to experimentally show the effectiveness of the proposed technique.

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