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A novel objective function with artificial ecosystem-based optimization for relieving the mismatching power loss of large-scale photovoltaic array

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dc.citedby56
dc.contributor.authorYousri D.en_US
dc.contributor.authorBabu T.S.en_US
dc.contributor.authorMirjalili S.en_US
dc.contributor.authorRajasekar N.en_US
dc.contributor.authorElaziz M.A.en_US
dc.contributor.authorid56688582500en_US
dc.contributor.authorid56267551500en_US
dc.contributor.authorid51461922300en_US
dc.contributor.authorid35090434600en_US
dc.contributor.authorid57195591068en_US
dc.date.accessioned2023-05-29T08:06:54Z
dc.date.available2023-05-29T08:06:54Z
dc.date.issued2020
dc.descriptionEcosystems; Health; Particle swarm optimization (PSO); Photovoltaic cells; Solar power plants; Artificial ecosystems; Objective functions; Optimization algorithms; Particle swarm optimizers; Photovoltaic arrays; Photovoltaic power; Uniform dispersions; Wilcoxon signed rank test; Solar power generationen_US
dc.description.abstractHarvesting maximum power from a partially shaded photovoltaic array is a critical issue that attracts the attention of several researchers. As per the literature, it is found that providing an optimal reconfigured pattern of the shaded photovoltaic array is an optimal solution for this issue. Therefore, in this paper, an innovative fitness function has been considered with the artificial ecosystem-based optimization for an electrical photovoltaic array reconfiguration approach. The proposed approach has been applied for the large scale photovoltaic arrays including 9 �9,6�20,16�16, and 25 � 25 photovoltaic array with different shade patterns. The new fitness function has been validated via a comparison with the regular used weighted function in literature. The quality of the solutions of the proposed artificial ecosystem-based optimization�reconfiguration approach has been assessed and demonstrated via performing several measures namely fill factor, percentage of power loss, mismatch power loss, and power enhancement in comparison with a total cross-tied, particle swarm optimizer approaches, and harris hawks optimizer. Furthermore, the Wilcoxon signed-rank test has been performed to illustrate the applicability, robustness, and consistency of the proposed algorithm results across several independent runs. The analysis reveals the quality of the innovative fitness function while integrating with the optimization algorithms in comparison to the weighted fitness function in producing higher power values via attaining a more efficient photovoltaic array design. Furthermore, the results confirmed the efficiency of the artificial ecosystem-based optimization�photovoltaic reconfiguration approach in boosting the generated photovoltaic power by a percentage of 28.688%, 7.0197 %, 29.2565%, 8.3811% and 5.3884 % across the considered systems with an uniform dispersion of the shadow on the photovoltaic surface and providing highest consistent in the maximum power values across the independent runs. � 2020 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo113385
dc.identifier.doi10.1016/j.enconman.2020.113385
dc.identifier.scopus2-s2.0-85090737570
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85090737570&doi=10.1016%2fj.enconman.2020.113385&partnerID=40&md5=6807ed6376357614dd02eb3e9c84a0e4
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25132
dc.identifier.volume225
dc.publisherElsevier Ltden_US
dc.relation.ispartofAll Open Access, Green
dc.sourceScopus
dc.sourcetitleEnergy Conversion and Management
dc.titleA novel objective function with artificial ecosystem-based optimization for relieving the mismatching power loss of large-scale photovoltaic arrayen_US
dc.typeArticleen_US
dspace.entity.typePublication
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