Photovoltaic panels have a non-linear current-voltage characteristics to produce the maximum power at only one point called the maximum power point. In the case of the uniform illumination a single solar panel shows only one maximum power, which is also the global maximum power point. In the case an irregularly illuminated photovoltaic panel many local maxima on the power-voltage curve can be observed and only one of them is the global maximum. The proposed algorithm detects whether a solar panel is in the uniform insolation conditions. Then an appropriate strategy of tracking the maximum power point is taken using a decision algorithm. The proposed method is simulated in the environment created by the authors, which allows to stimulate photovoltaic panels in real conditions of lighting, temperature and shading.
This research presents a comparative study for maximum power point tracking (MPPT) methodologies for a photovoltaic (PV) system. A novel hybrid algorithm golden section search assisted perturb and observe (GSS-PO) is proposed to solve the problems of the conventional PO (CPO). The aim of this new methodology is to boost the efficiency of the CPO. The new algorithm has a very low convergence time and a very high efficiency. GSS-PO is compared with the intelligent nature-inspired multi-verse optimization (MVO) algorithm by a simulation validation. The simulation study reveals that the novel GSS-PO outperforms MVO under uniform irradiance conditions and under a sudden change in irradiance.