感应电机模型预测控制中权重因子的离散优化

IF 5.2 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Open Journal of the Industrial Electronics Society Pub Date : 2023-11-28 DOI:10.1109/OJIES.2023.3333873

S. Alireza Davari;Vahab Nekoukar;Shirin Azadi;Freddy Flores-Bahamonde;Cristian Garcia;Jose Rodriguez

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引用次数: 0

摘要

调整权重因子对于建模预测转矩和磁链控制至关重要。本研究利用一组有限的离散加权因子来确定最优解。为了防止局部最优解的出现，采用了帕累托线优化技术。通过精度分析，优化离散加权因子的数量，减少迭代次数。采用定子电流畸变最小准则，从Pareto线求出最终全局最优解。基于4kw感应电机驱动试验台的实验数据，将所提出的优化方法与粒子群优化方法的结果进行了比较。该方法可以在较短的计算时间内实现全局最优加权因子，同时保持较低的总谐波失真和转矩脉动。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Discrete Optimization of Weighting Factor in Model Predictive Control of Induction Motor

Tuning the weighting factor is crucial to model predictive torque and flux control. A finite set of discrete weighting factors is utilized in this research to determine the optimum solution. The Pareto line optimization technique is implemented to prevent the occurrence of local optimum solutions. By conducting an accuracy analysis, the number of discrete weighting factors is optimized, and the number of iterations is reduced. The stator current distortion minimization criterion is used to obtain the ultimate global optimal solution from the Pareto line. This study compares the results of the proposed optimization method and the particle swarm optimization method based on experimental data from a 4 kW induction motor drive test bench. The proposed technique can achieve the global optimum weighting factor in a shorter computational duration while maintaining a slightly lower total harmonics distortion and torque ripple.

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来源期刊

IEEE Open Journal of the Industrial Electronics Society ENGINEERING, ELECTRICAL & ELECTRONIC-

CiteScore

10.80

自引率

2.40%

发文量

审稿时长

12 weeks

期刊介绍： The IEEE Open Journal of the Industrial Electronics Society is dedicated to advancing information-intensive, knowledge-based automation, and digitalization, aiming to enhance various industrial and infrastructural ecosystems including energy, mobility, health, and home/building infrastructure. Encompassing a range of techniques leveraging data and information acquisition, analysis, manipulation, and distribution, the journal strives to achieve greater flexibility, efficiency, effectiveness, reliability, and security within digitalized and networked environments. Our scope provides a platform for discourse and dissemination of the latest developments in numerous research and innovation areas. These include electrical components and systems, smart grids, industrial cyber-physical systems, motion control, robotics and mechatronics, sensors and actuators, factory and building communication and automation, industrial digitalization, flexible and reconfigurable manufacturing, assistant systems, industrial applications of artificial intelligence and data science, as well as the implementation of machine learning, artificial neural networks, and fuzzy logic. Additionally, we explore human factors in digitalized and networked ecosystems. Join us in exploring and shaping the future of industrial electronics and digitalization.