Advanced ANFIS Control Strategy for PV Grid Coupling in Rapidly Varying Irradiance and Temperature

Abstract

The need for photovoltaic (PV) systems to the electricity grid is becoming pertinent in past years driven by the global demand for sustainable energy and the necessity to reduce greenhouse gas (GHG) emissions. In contrast to the advantages that can be provided by solar photovoltaic systems, their installation creates a significant obstacle: the variability of solar energy caused by rapid variations in irradiance and temperature, which may impact the stability of electricity generation and the reliability of the grid. To minimize these complexities, a grid-connected PV system is given with an integrated control approach based on adaptive neuro-fuzzy inference systems (ANFIS), as proposed in this work. The suggested system can handle maximum power point tracking (MPPT) in challenging and fast-changing conditions using the learning capability of the neural network and the decision-making ability of fuzzy logic. The ANFIS controller adapt actively the duty cycle of a DC-DC step-up converter to boost the photovoltaic voltage to its maximum value, while a grid synchronization mechanism guaranties that the energy supplied can meet quality requirements and that the power factor remains unity. Simulation results founded using MATLAB/Simulink program validate the effectiveness of the approach and reveal that ANFIS outperforms classic methods in terms of MPPT convergence speed, steady-state oscillations, and tolerance to system parameter modifications.