Converters for induction motors enhancing fault tolerance in matrix: a hybrid EOO–RERNN approach

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
W. Vinil Dani, M. C. Jobin Christ
{"title":"Converters for induction motors enhancing fault tolerance in matrix: a hybrid EOO–RERNN approach","authors":"W. Vinil Dani, M. C. Jobin Christ","doi":"10.1007/s00202-024-02692-2","DOIUrl":null,"url":null,"abstract":"<p>This research presents a hybrid technique named EOO–RERNN, integrating the Eurasian oystercatcher optimizer (EOO) and Recalling enhanced recurrent neural network (RERNN), to enhance fault tolerance in Matrix converters (MCs) for Induction Motors (IMs). The proposed method assesses fault impacts, reconstructs healthy phases, manages switching frequency with Space vector modulation (SVM), and diagnoses faults to optimize switching states. Comparative analysis using MATLAB/Simulink shows a 1.1% reduction in torque ripple compared to existing methods like the Cuckoo Search Algorithm and Particle Swarm Optimization, demonstrating superior performance and improved motor reliability.</p>","PeriodicalId":50546,"journal":{"name":"Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electrical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s00202-024-02692-2","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

This research presents a hybrid technique named EOO–RERNN, integrating the Eurasian oystercatcher optimizer (EOO) and Recalling enhanced recurrent neural network (RERNN), to enhance fault tolerance in Matrix converters (MCs) for Induction Motors (IMs). The proposed method assesses fault impacts, reconstructs healthy phases, manages switching frequency with Space vector modulation (SVM), and diagnoses faults to optimize switching states. Comparative analysis using MATLAB/Simulink shows a 1.1% reduction in torque ripple compared to existing methods like the Cuckoo Search Algorithm and Particle Swarm Optimization, demonstrating superior performance and improved motor reliability.

Abstract Image

增强矩阵容错能力的感应电机变流器:EOO-RERNN 混合方法
本研究提出了一种名为 EOO-RERNN 的混合技术,它集成了欧亚捕蛎优化器(EOO)和召回增强型循环神经网络(RERNN),以提高感应电机(IM)矩阵转换器(MC)的容错能力。所提出的方法可评估故障影响、重建健康相位、利用空间矢量调制(SVM)管理开关频率以及诊断故障以优化开关状态。使用 MATLAB/Simulink 进行的比较分析表明,与布谷鸟搜索算法和粒子群优化等现有方法相比,转矩纹波降低了 1.1%,显示出卓越的性能和更高的电机可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Electrical Engineering
Electrical Engineering 工程技术-工程:电子与电气
CiteScore
3.60
自引率
16.70%
发文量
0
审稿时长
>12 weeks
期刊介绍: The journal “Electrical Engineering” following the long tradition of Archiv für Elektrotechnik publishes original papers of archival value in electrical engineering with a strong focus on electric power systems, smart grid approaches to power transmission and distribution, power system planning, operation and control, electricity markets, renewable power generation, microgrids, power electronics, electrical machines and drives, electric vehicles, railway electrification systems and electric transportation infrastructures, energy storage in electric power systems and vehicles, high voltage engineering, electromagnetic transients in power networks, lightning protection, electrical safety, electrical insulation systems, apparatus, devices, and components. Manuscripts describing theoretical, computer application and experimental research results are welcomed. Electrical Engineering - Archiv für Elektrotechnik is published in agreement with Verband der Elektrotechnik Elektronik Informationstechnik eV (VDE).
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信