Adaptive hybrid MPPT algorithms for grid connected PV system with solar irradiance and temperature variation

This study focuses on advancing the performance of grid-tied solar photovoltaic (PV) systems through the development of adaptive Maximum Power Point Tracking (MPPT) strategies. Instead of conventional approaches, the research employs cutting-edge learning algorithms to enhance the system’s ability to harvest maximum power in dynamically changing environmental conditions.The effectiveness of grid-connected photovoltaic (PV) systems that use an MPPT algorithm based on artificial neural networks (ANN) is examined in this study. This study looks at three different possible environmental states: (1) varying solar irradiance for a constant temperature; (2) varying temperatures for a constant irradiance; and (3) varying temperatures and irradiances simultaniously. Simulation results, executed using Matlab/Simulink, demonstrate the effectiveness of these intelligent algorithms in achieving superior power generation across a spectrum of environmental conditions. The proposed algorithm outperforms the aforementioned MPPT techniques in a number of important respects, including its rapid dynamic response time, low steady-state power oscillations, high power production efficiency, and ability to function effectively in environments with a great deal of environmental complexity.