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A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System

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dc.citedby30
dc.contributor.authorRoy R.B.en_US
dc.contributor.authorRokonuzzaman M.en_US
dc.contributor.authorAmin N.en_US
dc.contributor.authorMishu M.K.en_US
dc.contributor.authorAlahakoon S.en_US
dc.contributor.authorRahman S.en_US
dc.contributor.authorMithulananthan N.en_US
dc.contributor.authorRahman K.S.en_US
dc.contributor.authorShakeri M.en_US
dc.contributor.authorPasupuleti J.en_US
dc.contributor.authorid56603588300en_US
dc.contributor.authorid57190566039en_US
dc.contributor.authorid7102424614en_US
dc.contributor.authorid57192669693en_US
dc.contributor.authorid6508134705en_US
dc.contributor.authorid57194406794en_US
dc.contributor.authorid56246076300en_US
dc.contributor.authorid56348138800en_US
dc.contributor.authorid55433849200en_US
dc.contributor.authorid11340187300en_US
dc.date.accessioned2023-05-29T09:11:27Z
dc.date.available2023-05-29T09:11:27Z
dc.date.issued2021
dc.descriptionConjugate gradient method; Energy harvesting; Errors; Maximum power point trackers; Mean square error; Bayesian regularization algorithms; Comparative performance analysis; Energy harvesting systems; MATLAB/Simulink environment; Maximum Power Point Tracking; Scaled conjugate gradient algorithm; Scaled conjugate gradients; Solar photovoltaic system; Neural networksen_US
dc.description.abstractIn this paper, artificial neural network (ANN) based Levenberg-Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms are deployed in maximum power point tracking (MPPT) energy harvesting in solar photovoltaic (PV) system to forge a comparative performance analysis of the three different algorithms. A comparative analysis among the algorithms in terms of the performance of handling the trained dataset is presented. The MATLAB/Simulink environment is used to design the maximum power point tracking energy harvesting system and the artificial neural network toolbox is utilized to analyze the developed model. The proposed model is trained with 1000 dataset of solar irradiance, temperature, and voltages. Seventy percent data is used for training, while 15% data is employed for validation, and 15% data is utilized for testing. The trained datasets error histogram represents zero error in the training, validation, and test phase of data matching. The best validation performance is attained at 1000 epochs with nearly zero mean squared error where the trained data set is converged to the best training results. According to the results, the regression and gradient are 1, 1, 0.99 and 0.000078, 0.0000015739 and 0.26139 for Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient algorithms, respectively. The momentum parameters are 0.0000001 and 50000 for Levenberg-Marquardt and Bayesian Regularization algorithms, respectively, while the Scaled Conjugate Gradient algorithm does not have any momentum parameter. The Scaled Conjugate Gradient algorithm exhibit better performance compared to Levenberg-Marquardt and Bayesian Regularization algorithms. However, considering the dataset training, the correlation between input-output and error, the Levenberg-Marquardt algorithm performs better. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9481908
dc.identifier.doi10.1109/ACCESS.2021.3096864
dc.identifier.epage102152
dc.identifier.scopus2-s2.0-85110810737
dc.identifier.spage102137
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85110810737&doi=10.1109%2fACCESS.2021.3096864&partnerID=40&md5=3d61502efcc476f15b762115b5de553c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26520
dc.identifier.volume9
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.titleA Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV Systemen_US
dc.typeArticleen_US
dspace.entity.typePublication
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