کنفرانس بین المللی ماشین و محرکه های الکتریکی

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صفحه اصلی / icemd2023

Torque Fluctuation Minimization in PMa-SynRM Using Enhanced Field-Oriented Control by Genetic Algorithm Optimized Current Harmonic Injection

نویسندگان :

Hossein Dehghan-Niri¹ Ahmadreza Karami-Shahnani² Karim Abbaszadeh³ Reza Nasiri-Zarandi⁴ Mohammad Sedigh Toulabi⁵

1- Faculty of Electrical Engineering, K.N. Toosi University of Technology Tehran, Iran 2- Faculty of Electrical Engineering, K.N. Toosi University of Technology Tehran, Iran 3- Faculty of Electrical Engineering, K.N. Toosi University of Technology Tehran, Iran 4- Electric Machines Research Centre Niroo Research Institute Tehran, Iran 5- Department of Electrical and Computer Engineering University of Windsor Windsor, Canada

کلمات کلیدی :

Artificial Neural Network،Genetic-Algorithm،Torque،Fluctuation،PMa-SynRM

چکیده :

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.