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

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.

Atomic Orbital Search Algorithm for Efficient Maximum Power Point Tracking in Partially Shaded Solar PV Systems

“…The conventional algorithm, though simple and efficient in tracking the optimal value, failed when employed for tracking power under PSC. After their failure, artificial intelligence (AI)-based algorithms, such as fuzzy logic control (FLC) [8], artificial neural network (ANN) [9,10], etc., were employed. The algorithms were proven successful in tracking the maximum power under partially shaded conditions, but the training that they required posed a huge burden on the computer's memory.…”

Archimedes optimization algorithm based parameter extraction of photovoltaic models on a decent basis for novel accurate RMSE calculation

“…The comparative analysis reveals that the suggested method consistently yields superior results when compared to various optimization techniques, establishing its efficacy in accurately extracting parameters for the SDM in PV models. Another recent work proposed in (Chandrasekaran et al, 2023) (Hussain et al, 2023b) evaluates the performance of artificial neural network (ANN) algorithms for maximum power point tracking (MPPT) in solar PV systems. This investigation contributes valuable insights to the optimization landscape, complementing the innovative approach proposed in the primary study.…”