{"id":194910,"date":"2024-09-09T12:18:04","date_gmt":"2024-09-09T10:18:04","guid":{"rendered":"https:\/\/www.rse-web.it\/pubblicazioni\/a-machine-learning-based-tool-for-voltage-dip-classification\/"},"modified":"2024-09-09T12:19:19","modified_gmt":"2024-09-09T10:19:19","slug":"a-machine-learning-based-tool-for-voltage-dip-classification","status":"publish","type":"pubblicazioni","link":"https:\/\/www.rse-web.it\/en\/publications\/a-machine-learning-based-tool-for-voltage-dip-classification\/","title":{"rendered":"A Machine Learning Based Tool for Voltage Dip Classification"},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"<p>The paper presents a Machine Learning-based tool for making the ex-post analysis of voltage dips (VD) more automatic and effortless. The tool takes as input the full waveforms associated with voltage dips occurring in Italian MV networks and recorded by the QuEEN monitoring system implemented by RSE.<\/p>\n","protected":false},"author":464,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"tags":[1324,1407],"targets":[],"pubblicazioni_tipologie":[778],"class_list":["post-194910","pubblicazioni","type-pubblicazioni","status-publish","hentry","tag-electricity-network-en","tag-quality-of-supply","pubblicazioni_tipologie-paper-en"],"acf":{"dont_show_hompage":true,"projects":{"ID":191061,"post_author":"464","post_date":"2024-07-10 16:52:57","post_date_gmt":"2024-07-10 14:52:57","post_content":"","post_title":"Development and management of distribution grids","post_excerpt":"The project deals with methodologies and technologies suitable for fostering the development of smart distribution grids that can manage widespread distributed generation from renewable sources, facilitate integration between energy systems and ensure users high quality service.","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"development-and-management-of-distribution-grids","to_ping":"","pinged":"","post_modified":"2024-08-05 16:14:28","post_modified_gmt":"2024-08-05 14:14:28","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.rse-web.it\/progetti\/development-and-management-of-distribution-grids\/","menu_order":0,"post_type":"progetti","post_mime_type":"","comment_count":"0","filter":"raw"},"order_posts":"","dont_show_search":false,"related_posts":false,"show_on_slider":false,"single_post_data":{"titolo_spot":"","post_content":"<p>The first tool was developed to classify events based on their HV\/MV origin, since utilities will only be responsible for events due to failures occurring in their own networks; it uses Kalman Filter and Support Vector Machine (SVM) self-tuning to extract VD characteristics and classify events, respectively.<\/p>\n<p>On the other hand, the second tool, based on end-to-end Deep Learning, has been developed to distinguish between \u2018true\u2019 and \u2018false\u2019 voltage dips (VDs); it uses a Convolutional Neural Network (CNN) whose first layers are responsible for feature extraction while the last layers perform event classification.<\/p>\n","scarica_file":false,"link_estreno":[{"link_text":"Download Memory","link":"https:\/\/www.cired-repository.org\/handle\/20.500.12455\/235"}],"button":{"text":"","link":""},"referente_group":false,"data_emissione":"2019-06-06","autori":"H. Shadmehr, L. Tenti, R. Chiumeo","destinazione":"CIRED 2019, The 25th International Conference and Exhibition on Electricity Distribution, June 3-6, 2019","rif_rse":"18006134"},"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>A Machine Learning Based Tool for Voltage Dip Classification - 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\/publications\/integrated-approach-for-the-techno-economic-evaluation-of-transmission-infrastructures-application-to-the-hvdc-sa-co-i-project-318961\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A Machine Learning Based Tool for Voltage Dip Classification - RSE\" \/>\n<meta property=\"og:description\" content=\"The paper presents a Machine Learning-based tool for making the ex-post analysis of voltage dips (VD) more automatic and effortless. 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