A photovoltaic generator (PVG) has a nonlinear current-voltage characteristic with a special maximum power point (MPP), which depends on the environmental factors such as temperature and irradiation. In order to obtain maximum amount of energy from PVG, the power electronic converter connected to the PVG need to utilize some sort of technique for maximum power point tracking (MPPT). The aim of the thesis was to study different MPPT techniques to find the design rules, which offer the balance between the complexity and speed of the MPPT algorithm. Despite a significant amount of developed MPPT algorithms, perturbative MPPT algorithms and their corresponding improved versions were analyzed more thoroughly in this thesis due to the fact that they have been shown to provide good balance between complexity and MPPT performance. These algorithms were tested in steady-state and dynamic conditions.
The conventional perturbative MPPT algorithms have a drawback of trade-off between steady-state oscillations and fast dynamics. Therefore, the design variables the perturbation step size and the sampling frequency need to be optimized carefully to ensure proper operation yielding the highest possible efficiency. Sampling frequency of the perturbative algorithm should be selected as fast as possible to obtain the fastest dynamics in varying atmospheric conditions. However, the sampling frequency should not be selected faster than the PVG power settling time to guarantee that oscillatory behavior do not affect the decision process of perturbation sign. In contrast, the perturbation step-size has a significant effect on steady-state MPPT efficiency and on performance in dynamic atmospheric condition. To ensure proper operation in all atmospheric conditions, the power change in PVG caused by perturbation needs to be larger than the power change caused by any other external source such as irradiance variation, output voltage fluctuation and uncertainty factors in the measurement circuit.
Based on the simulations, high MPPT efficiency can be achieved even with conventional perturbative algorithms if these are properly optimized. Moreover, the experimental measurements have shown that the uncertainty factors such as noise and quantization errors in the measurement circuit play a significant role in the operation of perturbative algorithm. Therefore, the minimization of uncertainty must be focused on the noise sources that would influence most the decision process of the MPPT
