{"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}
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.
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).