Comparative Analysis of P&O and ANN MPPT Algorithms in Conventional and Floating Solar Photovoltaic Systems

Photovoltaic (PV) has proven to be the most reliable source of solar energy production. The performance of PV systems depends on sunlight and temperature, which fluctuate throughout the day. This results in fluctuations in the maximum power point (MPP) on the performance curve of the photovoltaic system. Therefore, various Maximum Power Point Tracking (MPPT) techniques are used to track the MPP and extract maximum performance from the photovoltaic system. The paper presents a comparative analysis of perturb and observe (P&O) maximum power point tracking (MPPT) and artificial neural network (ANN) algorithms in conventional and floating solar photovoltaic (PV) systems. The study assesses the performance and effectiveness of these algorithms in varying environmental conditions. Simulations are carried out in the MATLAB/Simulink environment. The results show that ANN performs better than R&D in both systems in this study.