Abstract
An accurate analytical model is adopted to estimate the torque ripple of a synchronous reluctance motor (SynRM). Desired behavior of the torque ripple functionin this motor is obtained by changing the angles of one and two flux barriers per pole (FBs) in the rotor. The torque ripple function of the SynRM serves as the multiple and close local optima. By identifying the behavior of this function, a comprehensive learning particle swarm optimization (CLPSO) algorithm (typically applied in solving multimodal functions), is adopted to reduce the torque ripple. The results indicate that compared to PSO (i.e. global optimization algorithms) the CLPSO algorithm is more efficient in torque ripple reduction and finding more local optima. Among the available optimal solutions with four FBs per pole, a sample is selected for motor construction. Finite element analysis and laboratory tests are performed to validate the results.
Highlights
To design a synchronous reluctance motor (SynRM), the rotor configuration, method of analyzing the motor performance, and optimization algorithm constitute the fundamental components
The performance of PSO and comprehensive learning particle swarm optimization (CLPSO) algorithms, appropriate for solving unimodal and multimodal problems, are compared for the optimization of the torque ripple of a SynRM using an analytical model to determine which algorithm is more appropriate for machine optimization
The results indicate that both algorithms yield the same global optimal solution, while the CLPSO algorithm finds more local optima than the PSO algorithm
Summary
To design a synchronous reluctance motor (SynRM), the rotor configuration, method of analyzing the motor performance, and optimization algorithm constitute the fundamental components. Instead of running repeated optimizations to assure the optimality of the solutions, the behavior of the torque ripple function for one or two FBs is determined using parametric analysis (incorporating an analytical model that allows rapid evaluation of motor performance). Variations of the motor torque(with the aforementioned three steps in the stator slot) and for the rotor of Fig. 2 in which only the second FB angle is reduced by one degree is shown in In both cases, the results of the analytical model are in good agreement with that of the FEA. As observed by changing one degree of the second FB arc, the torque ripple almost doubles, indicating the high sensitivity of the torque ripple to the end angles of the FBs; torque ripple optimization is necessary to determine the optimal angles
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