In-flow measurements of chemical markers in insulating oil (mineral, natural ester) and tuning the sensor sensitivity to the concentration range of interest are main issues addressed in this report concerning the development of optical sensors suitable for on-line analysis of degradation processes produced in specimens of the insulating system of oil-bath transformers assembled in RSE bench test. Further improvements in evaluating the effectiveness of Machine Learning applied to vibrational data of cast resin transformers for predictive analysis of the degradation profile are also reported.<\/p>\n","protected":false},"author":93,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"tags":[1530],"targets":[1317],"rapporti_tipologie":[762],"class_list":["post-195630","rapporti","type-rapporti","status-publish","hentry","tag-resin-transformers","targets-research","rapporti_tipologie-report-en"],"acf":{"dont_show_hompage":true,"projects":{"ID":188405,"post_author":"93","post_date":"2024-06-13 15:10:12","post_date_gmt":"2024-06-13 13:10:12","post_content":"","post_title":"Resilience and Security for Energy Systems","post_excerpt":"The goal of the project is to increase the security and resilience of the energy system in the face of greater environmental threats due to climate change, through the development of tools to support institutions and operators and the definition of an optimal set of actions to be adopted for this purpose.","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"resilience-and-security-for-energy-systems","to_ping":"","pinged":"","post_modified":"2024-07-02 10:30:30","post_modified_gmt":"2024-07-02 08:30:30","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.rse-web.it\/progetti\/resilience-and-security-for-energy-systems\/","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":"
In recent years, greater attention to safety and environmental protection policies has directed operators and utilities towards new “green” solutions of power transformers immersed in insulating oil, ester (natural or synthetic). Ester oil insulated power transformers can be pushed to operate at higher temperatures, with obvious advantages in terms of overload.<\/p>\n
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However, the degradation mechanisms of the solid ester oil-paper insulating system, especially under harsh operating conditions of the transformer, are still little studied and it is necessary to identify suitable techniques and devices for monitoring the by-products of paper degradation in ester oils (humidity and furans).<\/p>\n
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In medium voltage networks, where resin transformers are more widespread, the investigation into diagnostic methods suitable for detecting the degradation of solid insulation is still an open field of research today. The activities described in this report are aimed at an in-depth analysis of the issues started in the previous project.<\/p>\n
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As regards power transformers, the activities have focused on the development of new diagnostic systems, based on the dielectric and optical-chemical methods, to study the effects on the paper-ester insulating system of accelerated temperature aging, which will be reproduced in a test circuit set up at the RSE laboratories. In this report, the optical response to furans in insulating oil under static (drop) and dynamic (in flow) conditions was analysed.<\/p>\n
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A sensor system for the measurement of furans in different complex matrices, namely in different transformer oils, was also designed and implemented. The sensor system allows measurement of the analyte of interest with variable sensitivity, so that the concentration range of interest of furans can be changed depending on the matrix and\/or application.<\/p>\n
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Regarding fault diagnostics in resin transformers, further improvements in the application of vibration measurements and new machine learning methods for predictive analysis of the transformer degradation profile have been achieved.<\/p>\n
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The document is available on the site in Italian<\/strong><\/p>\n","link_estreno":false,"scarica_file":false,"button":{"text":"","link":""},"referente_group":false,"data_emissione":"2023-12-31","autori":"L. de Maria (RSE S.p.A.), S. Bittanti (Fondazione Politecnico Milano), L. Zeni (Universit\u00e0 Campania Luigi Vanvitelli), D. Merli, Giancarla Alberti (Universit\u00e0 di Pavia)","rapporto":"","rif_rse":"23012657"},"satellite_post_url":""},"yoast_head":"\n