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DC Field | Value | Language |
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dc.contributor.author | Sharma, Abhishek | - |
dc.contributor.author | Sharma, Abhinav | - |
dc.contributor.author | Dasgotra, Ankit | - |
dc.contributor.author | Jately, Vibhu | - |
dc.contributor.author | Ram, Mangey | - |
dc.contributor.author | Rajput, Shailendra | - |
dc.contributor.author | Averbukh, Moshe | - |
dc.contributor.author | Azzopardi, Brian | - |
dc.date.accessioned | 2022-06-23T08:49:54Z | - |
dc.date.available | 2022-06-23T08:49:54Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Sharma, A., Sharma, A., Dasgotra, A., Jately, V., Ram, M., Rajput, S., ... & Azzopardi, B. (2021). Opposition-based tunicate swarm algorithm for parameter optimization of solar cells. IEEE Access, 9, 125590-125602. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/98315 | - |
dc.description.abstract | Parameter estimation of photovoltaic modules is an essential step to observe, analyze, and optimize the performance of solar power systems. An efficient optimization approach is needed to obtain the finest value of unknown parameters. Herewith, this article proposes a novel opposition-based tunicate swarm algorithm for parameter estimation. The proposed algorithm is developed based on the exploration and exploitation components of the tunicate swarm algorithm. The opposition-based learning mechanism is employed to improve the diversification of the search space to provide a precise solution. The parameters of three types of photovoltaic modules (two polycrystalline and one monocrystalline) are estimated using the proposed algorithm. The estimated parameters show good agreement with the measured data for three modules at different irradiance levels. Performance of the developed opposition-based tunicate swarm algorithm is compared with other predefined algorithms in terms of robustness, statistical, and convergence analysis. The root mean square error values are minimum ( 6.83×10−4 , 2.06×10−4 , and 4.48×10−6 ) compared to the tunicate swarm algorithm and other predefined algorithms. Proposed algorithm decreases the function cost by 30.11%, 97.65%, and 99.80% for the SS2018 module, SolarexMSX-60 module, and Leibold solar module, respectively, as compared to the basic tunicate swarm algorithm. The statistical results and convergence speed depicts the outstanding performance of the anticipated approach. Furthermore, the Friedman ranking tests confirm the competence and reliability of the developed approach. | en_GB |
dc.description.sponsorship | This work was supported in part by the European Commission H2020 TWINNING Joint Universal activities for Mediterranean PV integration Excellence (JUMP2Excel) Project under Grant 810809. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | IEEE | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Photovoltaic cells | en_GB |
dc.subject | Electric power systems | en_GB |
dc.subject | Renewable energy sources | en_GB |
dc.subject | Building-integrated photovoltaic systems | en_GB |
dc.subject | Households -- Energy consumption | en_GB |
dc.subject | Machine learning | en_GB |
dc.subject | Metaheuristics | en_GB |
dc.title | Opposition-based tunicate swarm algorithm for parameter optimization of solar cells | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1109/ACCESS.2021.3110849 | - |
dc.publication.title | IEEE Access | en_GB |
Appears in Collections: | Scholarly Works - FacEngSCE |
Files in This Item:
File | Description | Size | Format | |
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Opposition-based tunicate swarm algorithm for parameter optimization of solar cells.pdf | 3.79 MB | Adobe PDF | View/Open |
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