Simulation of a Three-Phase Reluctance Motor in a Python Environment

This work introduces a simulation framework for analyzing the torques and currents of a three-phase reluctance motor within a Python environment. This project aims to extract and explore various parameters related to synchronous reluctance machines (SyRM), specifically focusing on torque and currents across phase angles. It is essential to address the high torque ripple issue that limits the motor’s application, and this research aims to address this technological drawback by leveraging artificial intelligence (AI) techniques, such as artificial neural networks and genetic algorithms, to minimize torque ripple. By integrating AI algorithms, including artificial neural networks and genetic algorithms, into the analysis, the study seeks to optimize the performance of synchronous reluctance machines and overcome the challenges associated with high torque ripple. The results and conclusions from this study will shed light on the potential of AI-based approaches, such as artificial neural networks and genetic algorithms, for mitigating torque inconsistencies and enhancing the applicability of reluctance motors.