Optimization of PMSM Design Parameters Using Update Meta-heuristic Algorithms

The design of PMSMs, which are frequently used in various areas of the industry and have strong features, is an important process because the design parameters significantly determine its performance and physical properties. The design of the-se motor contains complex equations and requires a lot of calculation load. The realization of the optimal design increases this complexity. In this study, it is aimed to perform the optimization of the design parameters of the PMSM motor in an easy way and to examine its effects on the motor performance. In the optimization process, the design parameters of a PMSM modeled by entering the initial values on the Ansys Maxwell program have been optimized with the optimization algorithm run on Matlab. In this process, Ansys Maxwell and Matlab program were run interactively by written scripts. Here, it is aimed to eliminate the need for the mathematical model of the motor in the optimization process and to ensure that the current optimization algorithms are easily used in the process of parameter optimization. Experimental studies were carried out for this purpose. In the experimental study, current meta-heuristic algorithms, Artificial Bee Colony algorithm and Symbiotic Organisms Search algorithms were used to optimize motor design parameters. At the end of the optimization process, the effects of optimized motor parameters on its performance and physical properties were examined comparatively. As a result, it has been observed that the proposed optimization method works in the process successfully and this method produces more accurate results than initial parameters computed from analytical method. In addition, the results obtained from the SOS algorithm have been observed to increase the performance of the engine more than the results obtained from ABC.