Multimodal Optimization Algorithm for Torque Ripple Reduction in Synchronous Reluctance Motors

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.

Automated summary

An accurate analytical model is used to estimate the torque ripple of a synchronous reluctance motor. By adjusting the angles of flux barriers in the rotor, desired ripple behavior is achieved. The comprehensive learning particle swarm optimization algorithm is employed to effectively reduce torque ripple and find more local optima.