Eddy Current Loss Calculation of High-Speed Permanent Magnet Machine With New Type of Powder Block Hierarchy Structure Rotor

IF 7.2 1区工程技术 Q1 AUTOMATION & CONTROL SYSTEMS

IEEE Transactions on Industrial Electronics Pub Date : 2025-01-16 DOI:10.1109/TIE.2024.3440516

Yue Zhang;Hao Luo;Huijun Wang;Guanglong Jia;Guangwei Liu

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引用次数: 0

Abstract

The high-speed permanent magnet machine (HSPMM) is faced with two key problems: the high rotor temperature rise caused by the rotor eddy current loss and the rotor linear speed limitation caused by the rotor stress concentration and the low tensile strength of magnetic materials. To solve the above problems, a HSPMM based on powder block hierarchical structure (PBHS) rotor is proposed in this article. To calculate the eddy current loss of PBHS rotor, an analytical calculation model of eddy current loss based on the penetration depth of multilayer composite structure is proposed, and the loss distribution of each part of HSPMM with PBHS rotor is calculated. Finally, the eddy current losses of the two rotors are compared through loss separation experiments. The comparison results indicate that PBHS rotor can effectively reduce rotor eddy current losses. The experimental results have verified the correctness of the loss analysis, providing a theoretical basis for the design and optimization of PBHS rotor HSPMM.

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新型粉末块级结构转子高速永磁电机涡流损耗计算

高速永磁电机（HSPMM）面临着两个关键问题：转子涡流损耗引起的转子温升过高和转子应力集中和磁性材料抗拉强度低引起的转子线速度限制。针对上述问题，本文提出了一种基于粉块分层结构（PBHS）转子的HSPMM。为了计算PBHS转子的涡流损耗，提出了基于多层复合材料结构侵彻深度的涡流损耗解析计算模型，并计算了PBHS转子HSPMM各部分的损耗分布。最后，通过损耗分离实验比较了两转子的涡流损耗。对比结果表明，PBHS转子能有效降低转子涡流损耗。实验结果验证了损耗分析的正确性，为PBHS转子HSPMM的设计与优化提供了理论依据。

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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来源期刊

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.