Prediction and Analysis of PWM-Induced Current Ripple of PMSM at Low Switching-to-Fundamental Frequency Ratios

IF 7.2 1区 工程技术 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Industrial Electronics Pub Date : 2024-08-21 DOI:10.1109/TIE.2024.3436682
Jiayue Zhou;Zhe Song;Xi Xiao
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Abstract

This article addresses the challenge of current ripple in permanent magnet synchronous machines (PMSMs), predominantly caused by the pulse width modulation (PWM) effect of voltage source inverter. This phenomenon is especially prominent at low switching-to-fundamental frequency ratios (SFFRs), leading to significant noises and decreased efficiency. This article proposes a flux-linkage-based discrete method to accurately and fastly predict PWM-induced current ripple in PMSMs. Ripple patterns and various influencing factors are studied in-depth based on the proposed method. Experimental studies to validate the proposed prediction method are presented, confirming its effectiveness, especially in scenarios where traditional methods fall short due to low SFFRs.
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低开关基频比时 PMSM 的 PWM 感应电流纹波预测与分析
本文讨论了电压源逆变器脉宽调制(PWM)效应引起的永磁同步电机电流纹波问题。这种现象在低开关-基频比(sffr)时尤其突出,导致显著的噪声和效率下降。本文提出了一种基于磁链的离散方法来准确、快速地预测pwm诱发的永磁同步电机电流纹波。在此基础上,对纹波模式和各种影响因素进行了深入研究。实验研究验证了该预测方法的有效性,特别是在传统方法因sffr低而无法实现的情况下。
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来源期刊
IEEE Transactions on Industrial Electronics
IEEE Transactions on Industrial Electronics 工程技术-工程:电子与电气
CiteScore
16.80
自引率
9.10%
发文量
1396
审稿时长
6.3 months
期刊介绍: Journal Name: IEEE Transactions on Industrial Electronics Publication Frequency: Monthly Scope: The scope of IEEE Transactions on Industrial Electronics encompasses the following areas: Applications of electronics, controls, and communications in industrial and manufacturing systems and processes. Power electronics and drive control techniques. System control and signal processing. Fault detection and diagnosis. Power systems. Instrumentation, measurement, and testing. Modeling and simulation. Motion control. Robotics. Sensors and actuators. Implementation of neural networks, fuzzy logic, and artificial intelligence in industrial systems. Factory automation. Communication and computer networks.
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