Torque Fluctuation Minimization in PMa-SynRM Using Optimized Current Harmonic Injection by Genetic Algorithm

This paper presents a comprehensive approach to significantly reduce torque fluctuation in permanent magnet-assisted synchronous reluctance motors (PMa-SynRM). This paper constructed a torque Fluctuation model utilizing a multi-layer perceptron (MLP) artificial neural network (ANN) based on the motor’s finite element method (FEM) simulation output. Subsequently, leveraging this ANN machine model, we propose an optimization strategy for harmonic injection utilizing a genetic algorithm (GA). The objective is to minimize torque Fluctuation through precise modulation of current harmonics. The study demonstrates the effectiveness of this integrated approach, showcasing notable reductions in torque Fluctuation and consequently enhancing the overall performance and efficiency of PMa-SynRM.