Abstract:

As the external factors contribute significantly to the effective functioning of photovoltaic (PV) systems, it is urgently needed that Maximum Power Point Tracking (MPPT) is the key to the fact that the greatest energy is produced. The present paper analyzes the application of the artificial intelligence (AI) methods to determine the Maximum Power Point (MPP) of dynamic photovoltaic (PV) generating installations. The machine learning and neural networks artificial intelligence technologies are employed to predict and regulate the maximum power point (MPP) under the varying irradiance and temperatures conditions alongside the conventional maximum power point tracking (MPPT) protocols. These artificial intelligence (AI) technologies enhance energy conservation and system responsiveness by the dynamic adaptation to changes in a real-life environmental scenario. Additionally, the paper also examines the application of the liquid immersion cooling principle to the solar cell structures in order to increase temperature control. The cooling system is to lessen the quantity of efficiency loss in the PV panels because of thermal considerations by installing the panels in a thermally conducting liquid, which has the effect of ensuring that sufficient heat is removed, and, therefore, that optimum operating temperatures are maintained. Liquid immersion cooling combined with AI-based Maximum Power Point Tracking (MPPT) can result in substantial increases in the power output and the energy consumption. empirical evidence indicates that smart control and improved cooling can achieve high reliability, higher overall system life, and higher generated power. The given article outlines the strategy of enhancing the energy efficiency and environmental friendliness of photovoltaic (PV) systems in the future, including artificial intelligence (AI) and novel cooling techniques