MPPT Based Artificial Neural Network Employing FOPI To Harvest The Maximum Power From A PV System Under Changing Irradiance of The City Ain El Ibel, Djelfa

Solar energy is a renewable energy source that has gained widespread use and efficiency, particularly in developing countries such as Algeria where solar irradiation is abundant. Photovoltaic (PV) systems have proven to be effective, leading to the implementation of numerous PV farms across Algeria, such as the PV farm in Ain El Ibel Djelfa. However, many of these systems rely on classical techniques like P&O and INC based MPPT, which are insufficient for varying weather conditions and can result in significant power loss. To address this issue, this study proposes a novel approach that utilizes an artificial neural network (ANN) with a fractional order PI controller to extract the maximum power from the PV panel. The proposed technique was tested and simulated using MATLAB/SIMULINK, with irradiance data obtained from the NASA Power, Prediction of Worldwide Energy Resources website for the city of Ain Ibel in August 2022. The results were compared to previous techniques and indicate that the ANN with fractional order PI controller approach yields better outcomes.