增强矩阵容错能力的感应电机变流器：EOO-RERNN 混合方法

IF 1.6 4区工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Electrical Engineering Pub Date : 2024-09-08 DOI:10.1007/s00202-024-02692-2

W. Vinil Dani, M. C. Jobin Christ

{"title":"增强矩阵容错能力的感应电机变流器：EOO-RERNN 混合方法","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":"62 1","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"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\":\"62 1\",\"pages\":\"\"},\"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}","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

摘要

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。

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

查看原文本刊更多论文

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

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Electrical Engineering 工程技术-工程：电子与电气

CiteScore

3.60

自引率

16.70%

发文量

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).