{"id":195703,"date":"2024-09-12T11:53:59","date_gmt":"2024-09-12T09:53:59","guid":{"rendered":"https:\/\/www.rse-web.it\/rapporti\/the-new-queen-site-new-pq-indicators-and-ml-techniques-for-voltage-sag-characterization\/"},"modified":"2024-09-12T11:56:48","modified_gmt":"2024-09-12T09:56:48","slug":"the-new-queen-site-new-pq-indicators-and-ml-techniques-for-voltage-sag-characterization","status":"publish","type":"rapporti","link":"https:\/\/www.rse-web.it\/en\/reports\/the-new-queen-site-new-pq-indicators-and-ml-techniques-for-voltage-sag-characterization\/","title":{"rendered":"The new QuEEN site: new PQ indicators and ML techniques for voltage sag characterization"},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"<p>The updated QuEEN monitoring system website provides users with results from some voltage sag characterization criteria based on Deep and Machine Learning and Higher Order Statistics (HOS), thereby contributing to the dissemination of research findings. The new tool also allows for the comparison of these criteria with those already implemented in QuEEN and which have also been incorporated into the national monitoring system developed by the Distributors.<\/p>\n","protected":false},"author":93,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"tags":[652,1324,1343,1338,1407],"targets":[1317],"rapporti_tipologie":[762],"class_list":["post-195703","rapporti","type-rapporti","status-publish","hentry","tag-active-networks","tag-electricity-network-en","tag-legislation","tag-power-quality-en","tag-quality-of-supply","targets-research","rapporti_tipologie-report-en"],"acf":{"projects":{"ID":191050,"post_author":"464","post_date":"2024-07-10 16:51:19","post_date_gmt":"2024-07-10 14:51:19","post_content":"","post_title":"Architecture and management models of the electrical system and grids","post_excerpt":"The project aims to develop methodologies, software tools, prototypes and demonstrators to optimise electric transmission and distribution grids, as well as new architecture and system management models and new regulatory schemes that encourage the integration of renewable and non-programmable generation, self-generation, storage systems and aggregators, and that take into account electric penetration.","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"architecture-and-management-models-of-the-electrical-system-and-grids","to_ping":"","pinged":"","post_modified":"2024-08-21 12:10:45","post_modified_gmt":"2024-08-21 10:10:45","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.rse-web.it\/progetti\/architecture-and-management-models-of-the-electrical-system-and-grids\/","menu_order":0,"post_type":"progetti","post_mime_type":"","comment_count":"0","filter":"raw"},"order_posts":"","dont_show_search":false,"related_posts":false,"dont_show_hompage":false,"show_on_slider":false,"single_post_data":{"titolo_spot":"","post_content":"<p>The report describes the activities that led to the update of the QuEEN monitoring system website. The new version of the site makes available to all users the results of applying innovative functions based on Deep and Machine Learning techniques for voltage sag characterization. However, the evaluation of Higher Order Statistics (HOS) for voltage sags has been implemented with access to results currently restricted to the distributors where the measuring equipment is installed. Originally, the QuEEN system was implemented to acquire knowledge of the average level of Power Quality in the Italian distribution network, starting from the voltage requirements established by the CEI EN 50160 standard.<\/p>\n<p>This update aims to help disseminate the results of the System Research to a broader audience and provides a tool to facilitate comparison between the new innovative functions for voltage sag characterization and the traditional methods already in place, as a result of the QuEEN experience, within the national monitoring system developed by the distributors. The update also included a redesign of the website, the addition of informational pages regarding the new functions, and the integration of additional features for system administrators.<\/p>\n","link_estreno":false,"scarica_file":[{"download_option":"download","file_name":"Download Report","download":{"ID":173526,"id":173526,"title":"21012161","filename":"21012161.pdf","filesize":10384719,"url":"https:\/\/www.rse-web.it\/wp-content\/uploads\/2022\/11\/21012161.pdf","link":"https:\/\/www.rse-web.it\/en\/rapporti\/il-nuovo-sito-queen-nuovi-indicatori-di-pq-e-tecniche-di-ml-per-la-caratterizzazione-dei-buchi-di-tensione\/attachment\/21012161\/","alt":"","author":"93","description":"","caption":"","name":"21012161","status":"inherit","uploaded_to":173525,"date":"2022-11-15 09:54:49","modified":"2022-11-15 09:54:49","menu_order":0,"mime_type":"application\/pdf","type":"application","subtype":"pdf","icon":"https:\/\/www.rse-web.it\/wp-includes\/images\/media\/document.png"}}],"button":{"text":"","link":""},"referente_group":false,"data_emissione":"2021-12-31","autori":"L. Tenti, R.G.M. Chiumeo, M. Zanoni, C. Chiappa, M. Volta (RSE S.p.A.)","rapporto":"","rif_rse":"21012161"},"satellite_post_url":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>The new QuEEN site: new PQ indicators and ML techniques for voltage sag characterization - RSE<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.rse-web.it\/en\/reports\/the-new-queen-site-new-pq-indicators-and-ml-techniques-for-voltage-sag-characterization\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The new QuEEN site: new PQ indicators and ML techniques for voltage sag characterization - RSE\" \/>\n<meta property=\"og:description\" content=\"The updated QuEEN monitoring system website provides users with results from some voltage sag characterization criteria based on Deep and Machine Learning and Higher Order Statistics (HOS), thereby contributing to the dissemination of research findings. 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