Application of AI-Based Algorithms for Industrial Photovoltaic Module Parameter Extraction
Solar energy is the best choice in non-renewable energy sources for generating electricity since it is a widely accessible and sustainable source. Solar energy is now among the useful substitute energy sources that readily exist on the energy market because of recent advances in photovoltaic (PV) ex...
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| Published in | SN computer science Vol. 4; no. 5; p. 525 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Singapore
Springer Nature Singapore
01.09.2023
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2661-8907 2662-995X 2661-8907 |
| DOI | 10.1007/s42979-023-02008-4 |
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| Abstract | Solar energy is the best choice in non-renewable energy sources for generating electricity since it is a widely accessible and sustainable source. Solar energy is now among the useful substitute energy sources that readily exist on the energy market because of recent advances in photovoltaic (PV) expertise. The enhancement of power efficiency of PV systems is a significant priority of the research community and industry to make solar energy more accessible and cost-effective. A solar cell's circuit model is non-linear and transcendental with some unknown parameters. The electrical equivalent circuit of industrial solar photovoltaic modules has been designed using the experimental results from the datasets. This paper compares novel AI-based algorithms for industrial photovoltaic module parameter extraction and presents detailed analysis, including state-of-the-art approaches. |
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| AbstractList | Solar energy is the best choice in non-renewable energy sources for generating electricity since it is a widely accessible and sustainable source. Solar energy is now among the useful substitute energy sources that readily exist on the energy market because of recent advances in photovoltaic (PV) expertise. The enhancement of power efficiency of PV systems is a significant priority of the research community and industry to make solar energy more accessible and cost-effective. A solar cell's circuit model is non-linear and transcendental with some unknown parameters. The electrical equivalent circuit of industrial solar photovoltaic modules has been designed using the experimental results from the datasets. This paper compares novel AI-based algorithms for industrial photovoltaic module parameter extraction and presents detailed analysis, including state-of-the-art approaches. |
| ArticleNumber | 525 |
| Author | Devarapalli, Ramesh Bali, Sravana Kumar Kumar, Vandrasi Raja |
| Author_xml | – sequence: 1 givenname: Vandrasi Raja orcidid: 0000-0002-4361-2926 surname: Kumar fullname: Kumar, Vandrasi Raja email: rvandras2@gitam.edu organization: Department of EEE, GITAM Deemed to be University – sequence: 2 givenname: Sravana Kumar surname: Bali fullname: Bali, Sravana Kumar organization: Department of EEE, GITAM Deemed to be University – sequence: 3 givenname: Ramesh surname: Devarapalli fullname: Devarapalli, Ramesh organization: Department of Electrical/Electronics and Instrumentation Engineering, Institute of Chemical Technology |
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| Cites_doi | 10.1016/j.apenergy.2017.12.115 10.3233/JIFS-189763 10.1080/01430750.2018.1517678 10.1080/15567036.2020.1755389 10.1016/j.solener.2017.10.063 10.1080/14786451.2013.863886 10.3934/naco.2020057 10.1002/cpe.6303 10.1016/j.apacoust.2019.107005 10.1080/01430750.2017.1421577 10.24425/acs.2021.138692 10.1080/01430750.2018.1501735 10.1016/j.eswa.2020.113917 10.1063/1.3122082 10.1109/TPEL.2012.2188818 10.1080/15567036.2020.1806956 10.1016/j.enconman.2020.112904 10.1002/adc2.45 10.1016/j.solener.2011.09.032 10.1049/el:19840281 10.1002/int.22535 10.1080/15567036.2018.1486480 10.1016/j.apenergy.2014.01.027 10.1080/13642819508239092 10.1016/j.enconman.2015.05.074 10.1007/s42452-021-04349-2 10.1080/01430750.2016.1144525 10.1080/15325008.2015.1014579 10.1080/15567036.2019.1648597 10.1049/el.2018.0402 10.1109/LED.2009.2013882 10.1016/j.renene.2018.07.152 10.1007/978-981-15-7675-1_3 10.1007/978-981-15-7571-6_15 10.1007/978-981-15-7675-1_48 10.1109/AICERA-ICMiCR.2013.6575948 |
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| Copyright | The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| SubjectTerms | Accessibility Algorithms Circuit design Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Diodes Enabling Innovative Computational Intelligence Technologies for IOT Equivalent circuits Females Heuristic Information Systems and Communication Service Literature reviews Optimization algorithms Original Research Parameter estimation Parameters Pattern Recognition and Graphics Photovoltaic cells Power efficiency Renewable energy sources Software Engineering/Programming and Operating Systems Solar cells Solar energy Vision |
| Title | Application of AI-Based Algorithms for Industrial Photovoltaic Module Parameter Extraction |
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