[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123262-en":3,"doc-seo-123262-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123262,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Machine Learning at the Grid Edge - Data-Driven Impedance Models for Model-Free Inverters","The future electric grid will rely on many smart, grid-tied inverters at the network edge, whose behavior is often characterized through impedance models. Traditional analytical impedance modeling requires complete and precise parameter knowledge and struggles to represent complex inverter functions; scalable online impedance measurement across many inverters and operating points is also difficult. InvNet is proposed as a machine learning framework that evaluates data-driven impedance-pattern models over a wide operating range with limited impedance data, using transfer learning to extrapolate across physics-based and real-world models and between different inverters. Comprehensive evaluations verify its effectiveness, and all data and models are open-sourced.","Aalborg Universitet  \nMachine Learning At the Grid Edge  \nData-Driven Impedance Models for Model-Free Inverters  \nLi, Yufei; Liao, Yicheng; Zhao, Liang; Chen, Minjie; Wang, Xiongfei; Nordstrom, Lars; Mittal, Prateek; Poor, H. Vincent  \nPublished in:  \nIEEE Transactions on Power Electronics  \nDOI (link to publication from Publisher):  \n10.1109/TPEL.2024.3399776  \nCreative Commons License  \nCC BY 4.0  \nPublication date: 2024  \nDocument Version  \nEarly version, also known as pre-print  \nLink to publication from Aalborg University  \nCitation for published version (APA):  \nLi, Y. , Liao, Y. , Zhao, L. , Chen, M. , Wang, X. , Nordstrom, L. , Mittal, P. , & Poor, H. V. (2024) . Machine Learning At the Grid Edge: Data-Driven Impedance Models for Model-Free Inverters. IEEE Transactions on Power  \nElectronics , 39(8), 10465-10481 . [https://doi.org/10.1109/TPEL.2024.3399776](https://doi.org/10.1109/TPEL.2024.3399776)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n-Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n-You may not further distribute the material or use it for any profit-making activity or commercial gain  \n-You may freely distribute the URL identifying the publication in the public portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact [us at vbn@aub.aau.dk](us at vbn@aub.aau.dk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from [vbn.aau.dk](vbn.aau.dk) on: March 09, 2025  \nMachine Learning at the Grid Edge: Data-Driven Impedance Models for Model-Free Inverters  \nThis paper was downloaded from TechRxiv ([https://www.techrxiv.org](https://www.techrxiv.org)) .  \nLICENSE  \nCC BY 4.0  \nSUBMISSION DATE / POSTED DATE 21-02-2023 / 28-08-2023  \nCITATION  \nLi , Yufei; Liao , Yicheng; Chen , Minjie; Wang , Xiongfei; Nordström , Lars; Mittal , Prateek; et al. (2023) . Machine Learning at the Grid Edge: Data-Driven Impedance Models for Model-Free Inverters. TechRxiv. Preprint.  \n[https://doi.org/10.36227/techrxiv.22137905.v2](https://doi.org/10.36227/techrxiv.22137905.v2)  \nDOI  \n10.36227/techrxiv.22137905.v2  \nMachine Learning at the Grid Edge: Data-Driven Impedance Models for Model-Free Inverters  \nYufei Li, Member, IEEE, Yicheng Liao, Member, IEEE, Liang Zhao, Student Member, IEEE, Minjie Chen, Senior Member, IEEE, Xiongfei Wang, Fellow, IEEE, Lars Nordstrm, Senior Member, IEEE, Prateek Mittal, Senior Member, IEEE, H. Vincent Poor, Fellow, IEEE  \nAbstract—The future electric grid is supported by a vast number of smart inverters interfacing with distributed energy resources at the edge. These inverters’ dynamics are typically characterized as impedances, which are crucial for ensuring grid stability and resiliency. However, the physical implementation of these inverters may change significantly from inverters to inverters and may be kept confidential. Existing analytical impedance models require a complete and precise understanding of system parameters. They can hardly capture the complete electrical behaviors when the inverters are performing complex functions. Online impedance measurements for many inverters across multiple operating points are not scalable. To address these issues, we present InvNet, a machine learning framework to systematically evaluate the effectiveness of data-driven methods for modeling inverter impedance patterns across a wide operation range, even with limited impedance data. Leveraging transfer learning, the InvNet can extrapolate from physics-based models to real-world ones and from one inverter to another with very limited data. This framework demonstrates machine learning asa ","cbCaioXioTxEYzt3","https://ap.wps.com/l/cbCaioXioTxEYzt3","pdf",11731282,1,16,"English","en",105,"# Abstract\n# Index Terms\n# Introduction","[{\"question\":\"Why are impedance models important for grid-tied smart inverters?\",\"answer\":\"Impedance characterization is crucial to ensure grid stability and resiliency when inverters interface with distributed energy resources at the grid edge.\"},{\"question\":\"What limitations affect existing analytical impedance modeling approaches?\",\"answer\":\"Analytical models require complete and precise system parameters and cannot accurately capture full electrical behaviors during complex inverter functions.\"},{\"question\":\"How does InvNet address inverter impedance modeling when impedance data is limited?\",\"answer\":\"InvNet uses a data-driven machine learning approach with transfer learning to extrapolate from physics-based models to real-world ones and to transfer knowledge between inverters using very limited data.\"}]","Machine Learning at the Grid Edge - Data-Driven Impedance Models for Model-Free Inverters | PDF",1785815533,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-at-the-grid-edge-data-driven-impedance-models-for-model-free-inverters","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-at-the-grid-edge-data-driven-impedance-models-for-model-free-inverters/123262/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are impedance models important for grid-tied smart inverters?","Question",{"text":75,"@type":76},"Impedance characterization is crucial to ensure grid stability and resiliency when inverters interface with distributed energy resources at the grid edge.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitations affect existing analytical impedance modeling approaches?",{"text":80,"@type":76},"Analytical models require complete and precise system parameters and cannot accurately capture full electrical behaviors during complex inverter functions.",{"name":82,"@type":73,"acceptedAnswer":83},"How does InvNet address inverter impedance modeling when impedance data is limited?",{"text":84,"@type":76},"InvNet uses a data-driven machine learning approach with transfer learning to extrapolate from physics-based models to real-world ones and to transfer knowledge between inverters using very limited data.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]