Abstract
Today, photovoltaic (PV) systems have turned into one of the best options to supply the load demand in developed countries thanks to their numerous benefits. The amount of active power generation in solar arrays effectively depends on their terminal voltage. To enhance the operation of PV systems, it is needed to use the maximum power point tracking methods known as MPPT algorithms. The conventional MPPT methods do not function properly in rapidly changing atmospheric conditions. An MPPT algorithm is developed is in the paper, utilizing the incremental conductance (INC) technique, together with a hybrid crow and pattern search (HCS-PS) algorithm based adaptive neuro-fuzzy inference system (ANFIS). The developed framework would be deployed to specify and track the MPP within a bi-step technique. The optimal voltages would be determined by employing the hybrid HCS-PS, considering various values of temperature and solar irradiance and used as the input-output training information set in the ANFIS in the first stage. The INC technique is applied for the tracking cycle process and begins an accurate tracking method from the mentioned point in the second stage. The power output of the solar PV panel is associated with substantial volatility, indicating the storage requirements. In this regard, a battery energy storage (BES) unit can be utilized along the solar panel. The simulation results confirm that the proposed HCS-PS-ANFIS-INC INC strategy can successfully decrease the convergence time and increase the output power under different conditions.