[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122936-en":3,"doc-seo-122936-105":30,"detail-sidebar-cat-0-en-105":83},{"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},122936,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Machine learning classification of power converter control mode","Ensuring reliable operation of the current and future electrical grid requires Transmission System Operators (TSOs) to verify that energy providers comply with grid codes and specifications. Many generation units connect through power electronic inverters operating in two main control modes: Grid Forming (GFM) and Grid Following (GFL). This work compares common machine learning algorithms for classifying converter control modes using frequency-domain admittance derived from external measurements. Results show strong accuracy within training structures, with random forest offering robustness to modified controls.","Machine learning classification of power converter control mode  \nRabah Ouali Jean-Yves Dieulot Pascal Yim Xavier Guillaud  \nFrédéric Colas Yang Wu Heng Wu  \nJanuary 19, 2024  \nAbstract  \nTo ensure the proper functioning of the current and future electrical grid, it is necessary for Transmission System Operators (TSOs) to verify that energy providers comply with the grid code and specifications provided by TSOs. Alot of energy production are conntected to the grid through a power electronic inverter. Grid Forming (GFM) and Grid Following (GFL) are the two types of operating modes used to control power electronic converters. The choice of control mode by TSOs to avoid impacting the stability of the grid is crucial, as is the commitment to these choices by energy suppliers. This article proposes a comparison between commonplace machine learning algorithms for converter control mode classification: GFL or GFM. The classification is based on frequency-domain admittance obtained by external measurement methods. Most algorithms are able to classify accurately when the control structure belongs to the training data, but they fail to classify modified control structures with the exception of the random forest algorithm.  \n0.1 Introduction  \nDue to increasing efforts to mitigate the effects of climate change, the electricity production sector is undergoing a decarbonization phase through the integration of carbon-neutral energy sources. The majority of these sources, such as photovoltaic and wind energy, are connected to the electrical grid using power electronic converters. This presents numerous challenges for the grid, including the transition from a network dominated by synchronous generators to one with a high penetration of distributed converters in a heterogeneous manner [1]. The behavior of a converter connected to the grid is closely tied to its control mode. It belongs to two main classes: Grid Forming (GFM) and Grid Following (GFL) [2]. Nowadays, the GFL control mode which makes the converter behave as a current source is widespread. However, The growing adoption of this converter control method in the production mix results in reduced inertia and heightened sensitivity of the converter to weak grids [3]. Another kind of control mode, called GFM allows the converter to behave asa voltage source which is able to offer voltage support to the grid. However, a high level of GFL or GFMin the grid can bring stability issues [4], and it is crucial for the Transmission System Operator (TSO) to choose, at a given location, in which mode a converter will operate. The TSO has to check whether the configuration of the converters are set accordingly. However, TSOs have a limited access to their structures and control parameters because converters are owned by private suppliers and are protected by intellectual property rights. Therefore, TSOs have to find out the dynamical behavior of the converters using external measurements. A modeling method based on measurements atthe Point of Common Coupling (PCC), using impedance (IM) or admittance (AM) models, has been proposed to analyze the behavior of converters connected to the grid without accessing the control and considering them as black-boxes [5] . In [6], Gong et al. presented an external measurement method to identify the impedance model ofa converter connected to the grid as a black-box. This method was validated by comparing its results with the analytical model obtained by a white-box approach. The measured impedance model was also used to analyze the stability of a single converter and the interaction between different converters connected to the same network [5] . However, the impedance results depend on the operating point and the value of the control parameters. Hence, to assess the performance of a converter across various operating points, it is essential to replicate the measurement procedure for each operating point. Qiu et al. [7] have developed a method to find the admitta","cbCaiu8bJj1OJnLK","https://ap.wps.com/l/cbCaiu8bJj1OJnLK","pdf",1226012,1,13,"English","en",105,"# Introduction\n## Power converter control modes and structures\n## External measurement and admittance modeling\n## Dataset generation and control parameter variation\n## Machine learning classification results","[{\"question\":\"What signal representation is used for classification in this study?\",\"answer\":\"Classification is based on frequency-domain admittance obtained from external measurement methods at the point of common coupling, treating the converter as a black box.\"}]","Machine learning classification of power converter control mode | PDF",1785813768,33,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"machine-learning-classification-of-power-converter-control-mode","",{"@graph":36,"@context":77},[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-classification-of-power-converter-control-mode/122936/",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],{"name":72,"@type":73,"acceptedAnswer":74},"What signal representation is used for classification in this study?","Question",{"text":75,"@type":76},"Classification is based on frequency-domain admittance obtained from external measurement methods at the point of common coupling, treating the converter as a black box.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]