Low switching loss drive for PMSM based on model predictive control

被引：0
|
作者：
Zhang Y. [1 ,2 ]
Qi R. [1 ]
机构：
[1] School of Automation, Northwestern Polytechnical University, Xi’an
[2] College of Electronic and Information Engineering, Hebei University, Baoding
来源：
Dianji yu Kongzhi Xuebao/Electric Machines and Control | 2023年 / 27卷 / 07期
关键词：
Lagrange multiplier; low switching loss; model predictive control; optimal algorithm; permanent magnet synchronous motor; two-goal optimization;
D O I：
10.15938/j.emc.2023.07.016
中图分类号：
学科分类号：
摘要：
In order to reduce the switching loss of permanent magnet synchronous motor (PMSM) controllers, a low switching loss drive based on model predictive control (LSL-MPC) was proposed, fully considering maximum voltage and current limitations. The MPC issue with the maximum voltage-current limitation was transformed to optimization with multiple inequality constraints. Besides, the conventional zero d-axis current strategy was utilized in this algorithm. In order to reduce the switching loss, the switching loss function of the voltage source inverter was introduced in the cost function of the optimization. But the introduction of switching loss functions led to the optimization problem becoming an unsolvable two-objective optimization problem. With the reasonable conversion, the two-goal optimization issue was transformed into a conventional optimization. The optimal switch sequence was calculated by minimizing the target function to reduce switching losses. The Lagrange multipliers in LSL-MPC can be online estimated. The experimental results indicate that the LSL-MPC could effectively reduce the switching loss with a low current ripple. Hence, the proposed LSL-MPC has certain advantages in improving the efficiency of the PMSM controller and energy-saving and emission reduction. © 2023 Editorial Department of Electric Machines and Control. All rights reserved.
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页码：155 / 162
页数：7
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