Real-Time Data Fetching Approach for Performance Evaluation of a DFIG Wind Power Generation System Using an IoT-Enabled Wind Emulator

Real-Time Data Fetching Approach for Performance Evaluation of a DFIG Wind Power Generation System Using an IoT-Enabled Wind Emulator | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Real-Time Data Fetching Approach for Performance Evaluation of a DFIG Wind Power Generation System Using an IoT-Enabled Wind Emulator Sitharthan Ramachandran, Rajesh M, Senthil Kumar R, Shanmuga Sundar Dhanabalan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7361080/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract The increased integration of wind energy into the power system network requires advanced testing and performance evaluation methods to ensure reliability and efficiency. This paper presents an IoT-based real-time data collection method for analyzing the performance of the Wind Power Generation System (WPGS) using an intelligent IoT-enabled wind emulator system. The proposed system uses IoT to gather comprehensive real-time wind data, which is processed by a digitally controlled emulator to accurately assess wind turbine responses under different wind conditions. The advantage of this system is that IoT and cloud-based data analytics enable predictive analysis of the WPGS, helping evaluate its behavior and performance under various real-time scenarios. The approach is tested with a wind emulator setup consisting of a 1kW Doubly-Fed Induction Generator (DFIG) connected to a Brushless DC (BLDC) motor, where the wind turbine model is developed on the VEE Pro platform, integrating an IoT-NodeRed, cloud API, and FPGA controller to simulate real-world wind conditions. Experimental results demonstrate up to 87% accuracy in modeling WPGS performance based on real-time wind profiles and achieve a 95% health index in predictive maintenance monitoring, greatly enhancing grid integration reliability. This study provides a cost-effective, scalable, and adaptable solution for real-time wind energy analysis, supporting ongoing research and development. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Wind Power Generation System Doubly-Fed Induction Generator Wind Emulator System Performance Evaluation Internet of Things Cloud FPGA Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 01 Oct, 2025 Reviews received at journal 30 Sep, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviews received at journal 29 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers invited by journal 24 Sep, 2025 Editor assigned by journal 23 Sep, 2025 Editor invited by journal 17 Sep, 2025 Submission checks completed at journal 18 Aug, 2025 First submitted to journal 18 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7361080","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":523165998,"identity":"26af57c7-e5c3-4d24-b747-9c850366e4bf","order_by":0,"name":"Sitharthan 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This paper presents an IoT-based real-time data collection method for analyzing the performance of the Wind Power Generation System (WPGS) using an intelligent IoT-enabled wind emulator system. The proposed system uses IoT to gather comprehensive real-time wind data, which is processed by a digitally controlled emulator to accurately assess wind turbine responses under different wind conditions. The advantage of this system is that IoT and cloud-based data analytics enable predictive analysis of the WPGS, helping evaluate its behavior and performance under various real-time scenarios. The approach is tested with a wind emulator setup consisting of a 1kW Doubly-Fed Induction Generator (DFIG) connected to a Brushless DC (BLDC) motor, where the wind turbine model is developed on the VEE Pro platform, integrating an IoT-NodeRed, cloud API, and FPGA controller to simulate real-world wind conditions. Experimental results demonstrate up to 87% accuracy in modeling WPGS performance based on real-time wind profiles and achieve a 95% health index in predictive maintenance monitoring, greatly enhancing grid integration reliability. This study provides a cost-effective, scalable, and adaptable solution for real-time wind energy analysis, supporting ongoing research and development.\u003c/p\u003e","manuscriptTitle":"Real-Time Data Fetching Approach for Performance Evaluation of a DFIG Wind Power Generation System Using an IoT-Enabled Wind Emulator","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-07 18:54:51","doi":"10.21203/rs.3.rs-7361080/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-01T04:10:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-30T09:06:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52595049977053763591392238795118193407","date":"2025-09-29T14:29:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107112286485706628381607285964586072059","date":"2025-09-29T10:35:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-29T10:06:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"247783625556595405091287160832206673661","date":"2025-09-24T10:39:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-24T10:31:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-23T10:38:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-17T10:00:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-18T14:35:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-18T14:31:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b433ad08-255a-41eb-9a8a-db0f3ad962a1","owner":[],"postedDate":"October 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55609863,"name":"Physical sciences/Energy science and technology"},{"id":55609864,"name":"Physical sciences/Engineering"},{"id":55609865,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-11-24T16:04:09+00:00","versionOfRecord":{"articleIdentity":"rs-7361080","link":"https://doi.org/10.1038/s41598-025-24234-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-18 15:58:44","publishedOnDateReadable":"November 18th, 2025"},"versionCreatedAt":"2025-10-07 18:54:51","video":"","vorDoi":"10.1038/s41598-025-24234-x","vorDoiUrl":"https://doi.org/10.1038/s41598-025-24234-x","workflowStages":[]},"version":"v1","identity":"rs-7361080","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7361080","identity":"rs-7361080","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}