Deep learning technology based maximum power point tracking in solar photovoltaic system

Abstract Maximum Power Point Tracking (MPPT) is essential for maximising energy harvest in solar photovoltaic (PV) systems. Despite their simplicity and widespread use, traditional approaches like Perturb and Observe (P&O) MPPT frequently suffer from poor tracking speeds, oscillations around the Maximum Power Point (MPP), and decreased effectiveness in dynamic environmental situations like quickly changing temperature or irradiance. This research investigates the use of deep learning-based MPPT in solar Photovoltaic (PV) systems in order to get beyond these restrictions. The suggested deep learning-based MPPT makes use of a pre-trained neural network that continually learns from operational and environmental data, in contrast to P&O, which depends on stepwise perturbation of voltage or current and only responds to changes that occur in real time. Because of this, it can forecast the ideal operating point in a variety of scenarios, leading to a quicker convergence to the MPP and less oscillations. The Deep learning based MPPT method shows better tracking accuracy and system efficiency with a power output of 50.3 kW under peak irradiance 1000 W m −2 compared to 48.5 kW for P&O. The integration of deep learning into MPPT not only boosts system efficiency but also opens up new possibilities for intelligent energy management in modern solar PV systems. MATLAB/SIMULINK software was used to create the grid-integrated solar PV system, and the performance of the MPPT output was evaluated for the new deep learning MPPT technique.