Design of the Adaptive Neuro-Fuzzy Inference System (ANFIS) and a Genetic Algorithm Controller for Solar Photovoltaic Systems Using the Boost Converter

Abstract

The Adaptive Neuro-Fuzzy Inference System (ANFIS) and an integrated offline Genetic Algorithm (GA) are proposed in this research. The ANFIS and GA technologies are employed to find the optimum point for maximum capacity in any environmental condition. Furthermore, a genetic algorithm is used to optimize data, and optimized values are used to train ANFIS. The power from the PV source is routed via the standard PI-controlled boost converter, and its working point always follows the MPPT point. When the ambient temperature and illuminance vary, the power may rapidly achieve a new maximum value thanks to the GA-ANFIS-MPPT P&O algorithm, which permits adaptive adjustment of the perturbation step. The new algorithm also reduces oscillation over the whole working point of the MPPT and enhances the boost converter's output voltage quality. The adaptive GA-ANFIS-MPPT P&O algorithm's benefit is validated by simulation on MATLAB has a module string, one parallel string, and a 250W PV panel. According to simulation results, the proposed GA-ANFIS-based MPPT can consistently and efficiently monitor the maximum power point of PV modules across various weather conditions