Improved design and analysis of MPPT technique for photovoltaic power systems to increase accuracy and speed under different conditions

Due to the many attractions of solar energy, a lot of research is being done in research centers to improve the usefulness of photovoltaic systems (PVs). Despite the widespread use of PVs in different societies, one of the biggest challenges of these sources is to obtain the maximum possible output power. Up to now, several investigations are performed to achieve the maximum power point (MPP) in PV systems. In this paper, an incremental conductance (INC) in the combination of farmland fertility optimization (IFFO) based on the adaptive neuro-fuzzy inference system (ANFIS) is applied to implement the MPPT controller. The proposed MPPT method applies hybrid IFFO in different temperatures and radiation situations to attain optimal voltages. Then, the ANFIS and the INC algorithm try to find the MPP as the ANFIS cannot find the precise point alone. The main advantage of the ANFIS and INC is providing less number of samples for training. Simulations are performed to confirm the advantage of the technique over the conventional approach. Achieving the global optimum point, lower oscillations around MPP and fast tracking are approved using the simulations with the suggested IFFO-ANFIS-INC method.