An Evaluation of ANN Algorithm Performance for MPPT Energy Harvesting in Solar PV Systems

Md Tahmid Hussain, Adil Sarwar, Mohd Tariq, Shabana Urooj (), Amal BaQais and Md. Alamgir Hossain ()
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Md Tahmid Hussain: Department of Electrical Engineering, ZHCET, Aligarh Muslim University, Aligarh 202002, India
Adil Sarwar: Department of Electrical Engineering, ZHCET, Aligarh Muslim University, Aligarh 202002, India
Mohd Tariq: Department of Electrical Engineering, ZHCET, Aligarh Muslim University, Aligarh 202002, India
Shabana Urooj: Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Amal BaQais: Department of Chemistry, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Md. Alamgir Hossain: Queensland Micro and Nanotechnology Centre, Griffith University, Nathan, QLD 4111, Australia

Sustainability, 2023, vol. 15, issue 14, 1-36

Abstract: In this paper, the Levenberg–Marquardt (LM), Bayesian regularization (BR), resilient backpropagation (RP), gradient descent momentum (GDM), Broyden–Fletcher–Goldfarb–Shanno (BFGS), and scaled conjugate gradient (SCG) algorithms constructed using artificial neural networks (ANN) are applied to the problem of MPPT energy harvesting in solar photovoltaic (PV) systems for the purpose of creating a comparative evaluation of the performance of the six distinct algorithms. The goal of this analysis is to determine which of the six algorithms has the best overall performance. In the study, the performance of managing the training dataset is compared across the algorithms. The maximum power point tracking energy harvesting system is created using the environment of MATLAB or Simulink, and the produced model is examined using the artificial neural network toolkit. A total of 1000 datasets of solar irradiance, temperature, and voltage were used to train the suggested model. The data are split into three categories: training, validation, and testing. Eighty percent of the total data is used for training the model, and the remaining twenty percent is divided equally for testing and validation. According to the results, the regression values of LM, RP, BR, and BFGS are 1, whereas the regression values for SCG and GDM are less than 1. The gradient values for LM, RP, BFGS, SCG, BR, and GDM are 7.983 × 10 −6 , 0.033415, 1.0211 × 10 −7 , 0.14161, 0.00010493, and 11.485, respectively. Similarly, the performance values for these algorithms are 2.0816 × 10 −10 , 2.8668 × 10 −6 , 9.98 × 10 −17 , 0.052985, 1.583 × 10 −7 , and 0.15378. Overall, the results demonstrate that the LM and BFGS algorithms exhibit superior performance in terms of gradient and overall performance. The RP and BR algorithms also perform well across various metrics, while the SCG and GDM algorithms show comparatively less effectiveness in addressing the proposed problem. These findings provide valuable insights into the relative performance of the six evaluated algorithms for MPPT energy harvesting in solar PV systems.

Keywords: artificial neural network (ANN); solar photovoltaic (PV); maximum power point tracking; Levenberg–Marquardt (LM); Bayesian regularization (BR); scaled conjugate gradient (SCG); resilient backpropagation (RP) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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