Despite a significant amount of developed MPPT algorithms, perturbative MPPT al-gorithms 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. However, the drawback of perturbative algo-rithms is the trade-off between steady-state oscillations and fast dynamics. Therefore, the design variables of the algorithm, the perturbation step size and the sampling pe-riod need to be optimized carefully to achieve highest possible efficiency and to ensure proper operation of the algorithm in all atmospheric conditions.
To achieve the fast dynamics in varying atmospheric conditions, sampling frequency of the perturbative algorithm should be selected as fast as possible. However, the sam-pling frequency should not be selected faster than the power settling time of a system, which can be obtained by analyzing the input voltage transient response of the system.
Otherwise, the algorithm samples voltage and current too quickly yielding incorrect operation and reduced energy yield. The second design variable in perturbative MPPT algorithms is the perturbation step-size. It has a significant effect on MPPT perfor-mance in dynamic atmospheric conditions and steady-state efficiency. The steady-state efficiency can be approximated to be directly proportional to squared perturbation step and therefore, the amplitude needs to be kept as low as possible. However, the lower limit of the perturbation depends on external factors affecting the PVG output power such as irradiance variation, output voltage fluctuation and uncertainty factors in the measurement circuit.
In fact, the uncertainty factors such as high-frequency switching ripple and quan-tization error of AD converters play significant role in the proper operation of the perturbative algorithms. Therefore, the minimization of uncertainty must be focused on the noise sources that would influence most the decision process of the MPPT. Since all the uncertainty factors cannot be analyzed, it is recommended to select the largest perturbation step, which produces the required MPPT efficiency. It has been shown that when the perturbation step stays below 5 % of the MPP voltage value in STC, the maximum steady-state efficiency stays higher than 99 %. Then the sampling fre-quency needs to be designed so that the algorithm is not confused during fast-changing irradiance conditions.
In conclusion, high MPPT efficiency can be achieved with conventional perturbative algorithms if these are properly optimized. However, if the adequate performance is not attained with these MPPT techniques in spite of optimization, fixed-step perturbative algorithms can be further improved. One of the introduced improvements is to use an adaptive-step algorithm, which can overcome the steady-state oscillations in traditional
perturbative methods. However, due the fact that the power prediction of these method are based also on two consecutive power samples, the simulations have been shown that robustness is poor in the most widely utilized adaptive-step algorithms in rapidly changing atmospheric conditions. The power prediction can be further improved by calculating one additional sample in the middle of the MPPT period, which ensures that drift phenomenon does not exist in fast-varying atmospheric conditions.
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