Paper Title
Levenberg Marquardt Algorithm-Based Enhanced MPPT using Artificial Neural Network

Abstract
The increased worldwide demand for renewable energy sources has facilitated the development of solar energy production within electricity markets. Maximum power point tracking (MPPT), despite its intermittent character, is a critical element in ensuring the efficient transfer of energy from the solar panel to the load. By utilizing a real-time dataset, this paper details the design and Matlab/Simulinkof an MPPT control based on an artificial neural network (ANN). The trained neural network uses irradiance, voltage, and current as inputs, and duty ratio as output. A comparison is made between the ANN method and the conventional method of MPPT, Perturb and Observe (P&O). The outcomes of the simulation demonstrate that MPPT based on ANN is more precise, provides a quicker response, and precludes the drift phenomenon. Keywords - Artificial Neural Network, Levenberg-Marquardt, Maximum Power Point, Photovoltaic.