Intelligence Artificial Algorithm-Based on Sliding Mode Control MPPT for a Photovoltaic System

The power-current relationship of a photovoltaic generator (GPV) is non-linear and contingent upon environmental factors. Nonetheless, achieving the highest possible power output from a GPV can only occur at a specific point along the characteristic curve. The development of Maximum Power Point Tracking (MPPT) techniques is fundamental to designing solar systems that optimize power generation. The Adaptive Fuzzy Neural Inference System (ANFIS) is one of the most effective ways to attain the maximum power point (MPP) in PV systems due to its prompt response time and minimal oscillations. Furthermore, sliding mode control (SMC) is a popular method for managing linear and nonlinear systems because of its robustness. The primary objective of this research is to introduce a novel approach that utilizes a combination of ANFIS and Sliding Mode Control (ANFIS-SMC) to safeguard the PV system against uncertain conditions and achieve the optimum power point. The simulation outcomes indicate that the ANFIS-SMC controller delivers a precise, swift, and resilient response, compared to other algorithms like perturb and observe (P&O).