{"title":"Design and Analysis of a Novel Asymmetric-Hybrid-Pole Variable Flux Memory Machine","authors":"Rui Tu;Hui Yang;Yixian Wang;Heyun Lin;Yiming Shen","doi":"10.1109/TIE.2024.3485713","DOIUrl":null,"url":null,"abstract":"In this article, a novel asymmetric-hybrid-pole variable flux memory machine (AHP-VFMM) is proposed and designed to realize satisfactory unintentional demagnetization (UD) withstand capability and high global efficiency, as well as lower flux regulation (FR) current level. The machine is structurally distinguished by a simple structure of asymmetric high coercive force (HCF) and low coercive force (LCF) permanent magnets (PMs). The main magnetic path of the proposed machine can be changed by manipulating the magnetization state (MS) of LCF PMs, resulting in significantly different field distributions and a large flux regulation range. The proposed AHP-VFMM and symmetric hybrid-pole VFMM (SHP-VFMM) are analyzed based on an equivalent magnetic circuit, and the design criteria of HCF PM in the AHP-VFMM to achieve better magnetic stability is deduced. Sensitive analysis is conducted to identify high- and low-sensitivity parameters of the AHP-VFMM. Then a two-layer optimization is conducted based on multi-objective genetic algorithm. The shape of LCF PM is further modified to improve the UD withstand capability. Afterwards, the AHP-VFMM is compared with an optimized SHP-VFMM with respect to electromagnetic and structural characteristics. Finally, an AHP-VFMM prototype is fabricated and tested to validate the feasibility of the proposed design.","PeriodicalId":13402,"journal":{"name":"IEEE Transactions on Industrial Electronics","volume":"72 5","pages":"5234-5245"},"PeriodicalIF":7.2000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Industrial Electronics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10745919/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In this article, a novel asymmetric-hybrid-pole variable flux memory machine (AHP-VFMM) is proposed and designed to realize satisfactory unintentional demagnetization (UD) withstand capability and high global efficiency, as well as lower flux regulation (FR) current level. The machine is structurally distinguished by a simple structure of asymmetric high coercive force (HCF) and low coercive force (LCF) permanent magnets (PMs). The main magnetic path of the proposed machine can be changed by manipulating the magnetization state (MS) of LCF PMs, resulting in significantly different field distributions and a large flux regulation range. The proposed AHP-VFMM and symmetric hybrid-pole VFMM (SHP-VFMM) are analyzed based on an equivalent magnetic circuit, and the design criteria of HCF PM in the AHP-VFMM to achieve better magnetic stability is deduced. Sensitive analysis is conducted to identify high- and low-sensitivity parameters of the AHP-VFMM. Then a two-layer optimization is conducted based on multi-objective genetic algorithm. The shape of LCF PM is further modified to improve the UD withstand capability. Afterwards, the AHP-VFMM is compared with an optimized SHP-VFMM with respect to electromagnetic and structural characteristics. Finally, an AHP-VFMM prototype is fabricated and tested to validate the feasibility of the proposed design.
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.