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

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dc.contributor.authorRoy, Rajib Baranen
dc.contributor.authorRokonuzzaman, Mden
dc.contributor.authorAmin, Nowshaden
dc.contributor.authorMishu, Mahmuda Khatunen
dc.contributor.authorAlahakoon, Sanathen
dc.contributor.authorRahman, Saifuren
dc.contributor.authorMithulananthan, Nadarajahen
dc.contributor.authorRahman, Kazi Sajeduren
dc.contributor.authorShakeri, Mohammaden
dc.contributor.authorPasupuleti, Jagadeeshen
dc.date.accessioned2024-03-27T19:11:41Zen
dc.date.available2024-03-27T19:11:41Zen
dc.date.issued2021-07-13en
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.en
dc.description.versionPublished versionen
dc.format.extentPages 102137-102152en
dc.format.extent16 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2021.3096864en
dc.identifier.eissn2169-3536en
dc.identifier.issn2169-3536en
dc.identifier.orcidRahman, Saifur [0000-0001-6226-8406]en
dc.identifier.urihttps://hdl.handle.net/10919/118473en
dc.identifier.volume9en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000678324200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectMathematical modelen
dc.subjectTrainingen
dc.subjectArtificial neural networksen
dc.subjectOscillatorsen
dc.subjectBiological neural networksen
dc.subjectMaximum power point trackersen
dc.subjectEnergy harvestingen
dc.subjectSolar photovoltaic (PV)en
dc.subjectenergy harvesting (EH)en
dc.subjectmaximum power point tracking (MPPT)en
dc.subjectartificial neural network (ANN)en
dc.subjectLevenberg-Marquardt (LM)en
dc.subjectBayesian regularization (BR)en
dc.subjectscaled conjugate gradient (SCG)en
dc.titleA Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV Systemen
dc.title.serialIEEE Accessen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Advanced Research Instituteen
pubs.organisational-group/Virginia Tech/Engineering/Electrical and Computer Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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