A simple and efficient hybrid maximum power point tracking method for PV systems under partially shaded condition

Maximum power point tracking (MPPT) is considered as an important strategy to increase the power output from photovoltaic (PV) systems. The presence of multiple maximum power points (MPPs) under partially shaded conditions (PSC) means that traditional MPPT algorithms, such as perturb and observe (P&O) and incremental conductance (IncCond), are unable to effectively track the global MPP. Other existing MPPT methods are either too complex or need additional equipment. In this paper, we propose a simple and efficient hybrid MPPT algorithm for PV systems working under PSC. It combines the advantages of P&O (or IncCond) and artificial neural network (ANN)-based MPPT methods, and features a relatively lower cost, fast response, and simple structure. In this hybrid MPPT, the ANN is used to initially categorize the power based on the irradiance pattern. Based on the classified category, an initial reference duty ratio is determined. Subsequently, a conventional MPPT algorithm (such as P&O or IncCond) is used to search near the global MPP. Finally, the system is able to accurately determine the global MPP which consequently increases the power output from the PV array. The effectiveness of the proposed hybrid MPPT, under various shading patterns, is verified using both simulations and an experimental setup.