The use of MPPT control is essential for the optimization of a photovoltaic system. It consists in controlling the static converter to reach the maximum power of the photovoltaic generator. Indeed, the MPP research is based on the variation of the duty cycle according to the evolution of the input parameters. the current and the voltage and consequently the power of the photovoltaic generator until the MPP is reached. The MPPT algorithm can be more or less complicated to find the MPP. In our paper we have presented three MPPT commands: perturb and observe, fuzzy logic, particle swarm optimization MATLAB/SIMIULINK is used. The results of the simulation illustrate the high tracking performance of the proposed technique under different climatic conditions.

The performance of a photovoltaic system is strongly affected by the environmental conditions which it is subjected such as random atmospheric variations. In order to improve the performance of a photovoltaic system, the work of this paper is devoted to the comparative study between the following MPPT algorithms: the perturbation and observation algorithm (P&O) and the particle swarm optimization algorithm PSO. These two algorithms are tested under various atmospheric conditions and evaluated in terms of efficiency, stability, speed, and robustness. The obtained simulation results show the effectiveness of the PSO than the P&O algorithm.