Recently updated on September 19th, 2024 at 01:17 pm<\/p>","protected":false},"excerpt":{"rendered":"
The NECP’s national objective for 2030 is to achieve a total photovoltaic (PV) capacity of 52 GW and a production of 74 TWh\/year. It is essential to identify efficient solutions and advanced tools for maintaining the performance of PV systems over time. For this purpose, RSE has created a PV Fault Facility for the validation of diagnostic algorithms, and has developed an algorithm for fault recognition. Finally, it analyzed the performance of some innovative solutions.<\/p>\n","protected":false},"author":93,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"tags":[1328,1307],"targets":[1317],"rapporti_tipologie":[762],"class_list":["post-192162","rapporti","type-rapporti","status-publish","hentry","tag-photovoltaics","tag-renewable-energy-en","targets-research","rapporti_tipologie-report-en"],"acf":{"projects":{"ID":191040,"post_author":"464","post_date":"2024-07-10 15:54:17","post_date_gmt":"2024-07-10 13:54:17","post_content":"","post_title":"High-efficiency photovoltaics","post_excerpt":"This project è aims to foster the development of highly efficient and reduced-cost photovoltaic (PV) generation systems, both flat-plate and concentrating, that also help to optimize production of energy from plants installed in Italy.","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"high-efficiency-photovoltaics","to_ping":"","pinged":"","post_modified":"2024-08-09 14:36:11","post_modified_gmt":"2024-08-09 12:36:11","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.rse-web.it\/progetti\/high-efficiency-photovoltaics\/","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":"
The national target set for 2030 in the NECP is to achieve an installed photovoltaic (PV) capacity of 52 GW with an output of 74 TWh\/year. Achieving this target requires the use of more efficient technologies and tools that can preserve the performance levels of PV systems without burdening the cost of electricity from PV (LCOE).
\nIn recent years, Machine Learning techniques have been applied to the development of fault identification and diagnosis (FDD) algorithms, as they can provide useful clues to Operation and Maintenance operators, and optimize maintenance time and costs.
\nAs part of the previous research activity [1], RSE designed and initiated the implementation of a PV Fault Facility capable of inducing the main fault conditions of a PV system [2]. As part of this work, RSE completed the implementation of the PV Fault Facility and developed a methodology for characterizing the soiling phenomenon of PV systems. The data from the fault tests, stored in a local database, were used to validate an FDD algorithm created under a research contract with Politecnico di Milano. The validation showed that the identification model is able to correctly report the presence of fault; the diagnosis model, based on two classifiers working at different irradiance ranges, proved to be a robust solution and very accurate in classifying fault types among those examined [1].
\nRSE also analyzed technological innovations that will enable increasingly efficient PV systems in the future. Specifically, the energy performance of bifacial technology and single-axis solar trackers were analyzed. As for the bifacial technology, an additional energy gain from the back contribution of bifacial PV modules was observed to average 15% during the monitoring year. As for trackers, possible solutions were identified to optimize solar tracking algorithms. Finally, when analyzing the bifacial technology installed on trackers, it was possible to observe how this solution achieved an energy gain during the summer months of over +35% compared to the single-sided solution on a fixed structure.<\/p>\n","link_estreno":false,"scarica_file":[{"download_option":"download","file_name":"Download Report","download":{"ID":171685,"id":171685,"title":"21010993","filename":"21010993.pdf","filesize":5269476,"url":"https:\/\/www.rse-web.it\/wp-content\/uploads\/2022\/09\/21010993.pdf","link":"https:\/\/www.rse-web.it\/en\/rapporti\/validazione-degli-algoritmi-di-rilevamento-e-diagnosi-dei-guasti-fv-analisi-della-tecnologia-bifacciale-e-dei-sistemi-di-inseguimento-solare\/attachment\/21010993\/","alt":"","author":"93","description":"","caption":"","name":"21010993","status":"inherit","uploaded_to":171684,"date":"2022-09-21 13:52:18","modified":"2022-09-21 13:52:18","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":"G. Maugeri, D. Bombelli, V. Urciuoli (RSE S.p.A.)","rapporto":"","rif_rse":"21010993"},"satellite_post_url":""},"yoast_head":"\n