Purpose
During the design process of synchronous reluctance motors (SynRMs), one crucial step, after its main dimensioning, is optimizing the rotor geometry for maximum average torque and minimum torque ripple. However, because of the complexity of rotor flux-barrier layers geometry, the number of rotor geometrical parameters is high and this step could be quite complex and time-consuming. To obtain a good performance, one needs a robust algorithm to optimize the rotor geometry. The purpose of this paper is to present a sequential iterative method for rotor shape optimization in SynRMs based on the per-unit rotor model to maximize the average torque and minimize the torque ripple.
Design/methodology/approach
In the presented method, at first, rotor geometrical parameters are classified into several groups based on their geometrical similarities, and then optimization is done on these individual groups iteratively. The method starts with an arbitrary feasible rotor geometry and proceeds to optimize it. Because the method’s performance depends on initial rotor geometry, different cases are studied to investigate the convergence and robustness of the method. The MATLAB software is used to implement the optimization algorithm, and the ANSYS Maxwell software is used for the finite element analysis.
Findings
The performance of the proposed method is studied on a three-phase 0.75 kW-1,500 rpm permanent magnet assisted SynRM. The results show that the method improves the average torque while reducing the torque ripple. Even if the method starts with an inappropriate initial rotor geometry, it is robust enough and converges within an acceptable number of iterations.
Originality/value
The value of this paper is in introducing a per-unit rotor model. When the authors optimize the rotor geometry for a specific motor rating, it can be scaled up or down for other ratings with little effort. In this work, the number of rotor poles is four and the number of rotor flux-barrier layers per pole is three. Other combinations could be analyzed in future studies.