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

Rajib Baran Roy,Nowshad Amin,Sanath Alahakoon,Md Rokonuzzaman,Jagadeesh Pasupuleti,Nadarajah Mithulananthan,Saifur Rahman,Mahmuda Khatun Mishu,Mohammad Shakeri,Kazi Sajedur Rahman

doi:10.1109/access.2021.3096864

Abstract

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.

Highlights

Solar PV energy is an integral part of our energy use and a vital component of renewable energy networks
The findings demonstrate that utilizing actual data, the optimized feedforward artificial neural network (ANN) methodology based on the Particle Swarm Optimization (PSO) algorithm effectively forecasts the highest power point, with hourly average efficiencies of more than 99.67% and 99.30% on sunny and cloudy days, respectively
This paper proposes a novel approach for a comparative performance analysis of three ANN algorithms namely Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient algorithms for maximum power point tracking (MPPT) energy harvesting in a solar PV system

Summary

INTRODUCTION

Solar PV energy is an integral part of our energy use and a vital component of renewable energy networks. A holistic approach is used for analyzing the performance parameters of three ANN algorithms with training, validation, and testing of the real dataset of solar irradiance, temperature, and generated voltage. It offers an in-depth analysis of artificial neural network data extraction and training Another ANN MPPT [55] shows better results than the climbing algorithms. It needs to build a PV cell simulation model to permit the solar panels maximum power point to perform their photoelectric conversion efficiency by influencing the solar panels production capacity by their light intensity and the outside temperature. Equation (1) can be rewritten by (2) if the thermal voltage of the array can be replaced as, Vt

ANN-BASED MPPT FOR SOLAR PV SYSTEM

RESULTS AND DISCUSSIONS

CONCLUSION