Performance improvement of maximum power point tracking for photovoltaic system using grasshopper optimization algorithm based ANFIS under different conditions

Objective: Power systems have been accommodating solar energy to a large extent, particularly when it comes to photovoltaic (PV) systems. In this regard, a number of parameters, including temperature and solar irradiance, have a significant impact on the power output of such systems. Maximum power point tracking (MPPT) strategies have been devised and deployed in PV systems as a successful remedy to boost efficiency in response to varying climate parameters. Accordingly, a promoted MPPT strategy is designed in this paper deploying the incremental conductance (INC) method optimized by employing the grasshopper optimization algorithm (GOA)-based adaptive neuro-fuzzy inference system (ANFIS).Methods: This technique includes two stages: specifying the optimal values of voltage by deploying the GOA taking into consideration various scenarios of temperature and solar irradi-ance. Afterward, the ANFIS would give the optimal value of the voltage by using solar irradiance on PV panels. It is noteworthy that the INC technique would initialize at this value and seek the maximum power point (MPP). One of the merits associated with using the ANFIS-based INC technique is the smaller dataset needed for the training purpose. In this regard, in case the ANFIS does not specify the definite point, the INC approach would do it.Result: Simulations are performed to validate the usefulness of the suggested GOA-ANFIS-based INC MPPT technique and compared with conventional MPPT schemes.