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

Conjugate 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; Scal...

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Main Authors: Roy R.B., Rokonuzzaman M., Amin N., Mishu M.K., Alahakoon S., Rahman S., Mithulananthan N., Rahman K.S., Shakeri M., Pasupuleti J.
Other Authors: 56603588300
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-265202023-05-29T17:11:27Z A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System Roy R.B. Rokonuzzaman M. Amin N. Mishu M.K. Alahakoon S. Rahman S. Mithulananthan N. Rahman K.S. Shakeri M. Pasupuleti J. 56603588300 57190566039 7102424614 57192669693 6508134705 57194406794 56246076300 56348138800 55433849200 11340187300 Conjugate 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 networks In 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. Final 2023-05-29T09:11:27Z 2023-05-29T09:11:27Z 2021 Article 10.1109/ACCESS.2021.3096864 2-s2.0-85110810737 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110810737&doi=10.1109%2fACCESS.2021.3096864&partnerID=40&md5=3d61502efcc476f15b762115b5de553c https://irepository.uniten.edu.my/handle/123456789/26520 9 9481908 102137 102152 All Open Access, Gold Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Conjugate 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 networks
author2 56603588300
author_facet 56603588300
Roy R.B.
Rokonuzzaman M.
Amin N.
Mishu M.K.
Alahakoon S.
Rahman S.
Mithulananthan N.
Rahman K.S.
Shakeri M.
Pasupuleti J.
format Article
author Roy R.B.
Rokonuzzaman M.
Amin N.
Mishu M.K.
Alahakoon S.
Rahman S.
Mithulananthan N.
Rahman K.S.
Shakeri M.
Pasupuleti J.
spellingShingle Roy R.B.
Rokonuzzaman M.
Amin N.
Mishu M.K.
Alahakoon S.
Rahman S.
Mithulananthan N.
Rahman K.S.
Shakeri M.
Pasupuleti J.
A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System
author_sort Roy R.B.
title A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System
title_short A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System
title_full A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System
title_fullStr A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System
title_full_unstemmed A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System
title_sort comparative performance analysis of ann algorithms for mppt energy harvesting in solar pv system
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806428301107920896
score 13.211869

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