{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:26:31Z","timestamp":1780359991348,"version":"3.54.1"},"reference-count":37,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T00:00:00Z","timestamp":1645574400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In this paper, a monitoring method for DC-DC converters in photovoltaic applications is presented. The primary goal is to prevent catastrophic failures by detecting malfunctioning conditions during the operation of the electrical system. The proposed prognostic procedure is based on machine learning techniques and focuses on the variations of passive components with respect to their nominal range. A theoretical study is proposed to choose the best measurements for the prognostic analysis and adapt the monitoring method to a photovoltaic system. In order to facilitate this study, a graphical assessment of testability is presented, and the effects of the variable solar irradiance on the selected measurements are also considered from a graphical point of view. The main technique presented in this paper to identify the malfunction conditions is based on a Multilayer neural network with Multi-Valued Neurons. The performances of this classifier applied on a Zeta converter are compared to those of a Support Vector Machine algorithm. The simulations carried out in the Simulink environment show a classification rate higher than 90%, and this means that the monitoring method allows the identification of problems in the initial phases, thus guaranteeing the possibility to change the work set-up and organize maintenance operations for DC-DC converters.<\/jats:p>","DOI":"10.3390\/a15030074","type":"journal-article","created":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T09:34:38Z","timestamp":1645608878000},"page":"74","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Machine Learning-Based Monitoring of DC-DC Converters in Photovoltaic Applications"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9749-602X","authenticated-orcid":false,"given":"Marco","family":"Bindi","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio","family":"Corti","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, University of Perugia, 06125 Perugia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Igor","family":"Aizenberg","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Manhattan College, Riverdale, NY 10471, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8697-2091","authenticated-orcid":false,"given":"Francesco","family":"Grasso","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7987-0487","authenticated-orcid":false,"given":"Gabriele Maria","family":"Lozito","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4319-1495","authenticated-orcid":false,"given":"Antonio","family":"Luchetta","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9955-1990","authenticated-orcid":false,"given":"Maria Cristina","family":"Piccirilli","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1921-6568","authenticated-orcid":false,"given":"Alberto","family":"Reatti","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1109\/TSG.2014.2359004","article-title":"Real-Time Energy Storage Management for Renewable Integration in Microgrid: An Off-Line Optimization Approach","volume":"6","author":"Rahbar","year":"2015","journal-title":"IEEE Trans. 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