Performance Analysis of Levenberg-Marquardt algorithm-based Neural Network MPPT of a Solar PV System

The machine learning technique is an upcoming important tool for optimizing the performance of solar PV systems. This paper investigates a neural network-based Levenberg-Marquardt (LM) algorithm to optimize the maximum power point tracking techniques (MPPT) power generation in the solar PV model and evaluates their performance. Using MPPT, we enhance the efficiency of the proposed solar system. The maximum power point (MPP) function is considered under environmental irradiation and temperature conditions. The research investigates the MPPT operation condition with supervised learning approach algorithms. The regression, gradient, and momentum parameter values for the LM methods are 1, 0.000000456, and 0.0000001. The results show that the LM method performs better when considering training datasets, input-output correlation, and error. The obtaining output of the MPPT energy system is designed in MATLAB/SIMULINK and analyzed using the artificial neural network toolkit.