Solar photovoltaic (SPV) systems are an environmentally friendly and recyclable source of renewable energy. Direct connection of solar panels to the load results in suboptimal power provision. Therefore, getting the maximum performance from the SPV system is essential to improve efficiency. Various techniques have been proposed to track the maximum power point (MPPT) of the SPV system. Traditional MPPT techniques are usually limited to uniform weather conditions. This paper presents a comprehensive comparative analysis of Maximum Power Point Tracking (MPPT) techniques employed in conventional and floating solar photovoltaic (PV) systems. The study examines various MPPT techniques, including perturb and observe (P&O), particle swarm optimization (PSO), and artificial neural networks (ANN), in both conventional and floating solar photovoltaic systems. The simulations were performed in a MATLAB/Simulink environment. The results of the comparison of MPPT algorithms in this study show that all these algorithms display very high-efficiency rates, generally above 97%, indicating good overall performance of MPPT systems. Still, the ANN and PSO techniques remain at the top. It is also worth noting that FPV systems tend to produce more power than LPV systems, particularly in the summer.
Chayma et al. (Sun,) studied this question.