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Characterization of PV panel and global optimization of its model parameters using genetic algorithm

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dc.citedby271
dc.contributor.authorIsmail M.S.en_US
dc.contributor.authorMoghavvemi M.en_US
dc.contributor.authorMahlia T.M.I.en_US
dc.contributor.authorid9633224700en_US
dc.contributor.authorid7003701545en_US
dc.contributor.authorid56997615100en_US
dc.date.accessioned2023-12-29T07:45:15Z
dc.date.available2023-12-29T07:45:15Z
dc.date.issued2013
dc.description.abstractThis paper details an improved modeling technique for a photovoltaic (PV) module; utilizing the optimization ability of a genetic algorithm, with different parameters of the PV module being computed via this approach. The accurate modeling of any PV module is incumbent upon the values of these parameters, as it is imperative in the context of any further studies concerning different PV applications. Simulation, optimization and the design of the hybrid systems that include PV are examples of these applications. The global optimization of the parameters and the applicability for the entire range of the solar radiation and a wide range of temperatures are achievable via this approach. The Manufacturer's Data Sheet information is used as a basis for the purpose of parameter optimization, with an average absolute error fitness function formulated; and a numerical iterative method used to solve the voltage-current relation of the PV module. The results of single-diode and two-diode models are evaluated in order to ascertain which of them are more accurate. Other cases are also analyzed in this paper for the purpose of comparison. The Matlab-Simulink environment is used to simulate the operation of the PV module, depending on the extracted parameters. The results of the simulation are compared with the Data Sheet information, which is obtained via experimentation in order to validate the reliability of the approach. Three types of PV modules, using different technologies, are tested for the purpose of this validation, and the results confirm the accuracy and reliability of the approach developed in this study. The effectiveness of the model developed by this approach to predict the performance of the PV system under partial shading conditions was also validated. � 2013 Elsevier Ltd. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.enconman.2013.03.033
dc.identifier.epage25
dc.identifier.scopus2-s2.0-84877764031
dc.identifier.spage10
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84877764031&doi=10.1016%2fj.enconman.2013.03.033&partnerID=40&md5=a7ea385fd75db639cc6b359efd59d1fc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30177
dc.identifier.volume73
dc.pagecount15
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleEnergy Conversion and Management
dc.subjectGenetic algorithm
dc.subjectPartial shading
dc.subjectPV modeling
dc.subjectRenewable energy
dc.subjectSolar energy
dc.subjectGlobal optimization
dc.subjectHybrid systems
dc.subjectIterative methods
dc.subjectOptimization
dc.subjectPhotovoltaic cells
dc.subjectSolar energy
dc.subjectSun
dc.subjectAverage absolute error
dc.subjectManufacturer's datum
dc.subjectNumerical iterative methods
dc.subjectOptimization ability
dc.subjectParameter optimization
dc.subjectPartial shading
dc.subjectRenewable energies
dc.subjectVoltage-current relations
dc.subjectGenetic algorithms
dc.titleCharacterization of PV panel and global optimization of its model parameters using genetic algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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