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DC Field | Value | Language |
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dc.contributor.author | Sharma, Abhishek | - |
dc.contributor.author | Dasgotra, Ankit | - |
dc.contributor.author | Tiwari, Sunil Kumar | - |
dc.contributor.author | Sharma, Abhinav | - |
dc.contributor.author | Jately, Vibhu | - |
dc.contributor.author | Azzopardi, Brian | - |
dc.date.accessioned | 2022-06-23T09:13:02Z | - |
dc.date.available | 2022-06-23T09:13:02Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Sharma, A., Dasgotra, A., Tiwari, S. K., Sharma, A., Jately, V., & Azzopardi, B. (2021). Parameter extraction of photovoltaic module using tunicate swarm algorithm. Electronics, 10(8), 878. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/98316 | - |
dc.description.abstract | In the renewable energy sector, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction is a non-linear complex optimization problem for solar PV cells. In this research work, the authors have implemented the Tunicate swarm algorithm (TSA) to estimate the optimized value of the unknown parameters of a PV cell/module under standard temperature conditions. The simulation results have been compared with four different, pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA), sine cosine (SCA), and whale optimization (WOA). The comparison of results broadly demonstrates that the TSA algorithm outperforms the existing optimization algorithms in terms of root mean square error (RMSE) and convergence rate. Furthermore, the statistical results confirm that the TSA algorithm is a better algorithm in terms of average robustness and precision. The Friedman ranking test is also carried out to demonstrate the competency and reliability of the implemented approach. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | MDPI | 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.subject | Swarm intelligence | en_GB |
dc.title | Parameter extraction of photovoltaic module using tunicate swarm algorithm | 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 | 10.3390/electronics10080878 | - |
dc.publication.title | Electronics | en_GB |
Appears in Collections: | Scholarly Works - FacEngSCE |
Files in This Item:
File | Description | Size | Format | |
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Parameter extraction of photovoltaic module using tunicate swarm algorithm.pdf | 1.64 MB | Adobe PDF | View/Open |
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