Adaptive neuro fuzzy inference systems based maximum power point tracking for a photovoltaic system connected to a grid

Abstract

This paper presented an adaptive neuro fuzzy inference system (ANFIS) for photovoltaic (PV) connected to the grid system for fast and precise maximum power point tracking (MPPT) in various weather conditions. Due to the high cost of sensors and the inaccuracy of irradiance readings, this study suggested an ANFIS approach for tracking maximum power without irradiance and voltage sensors. Instead, it relied on an equation to estimate the irradiance value. The generated power by PV is converted to the grid via a voltage source converter (VSC). MATLAB/SIMULINK has been used to simulate the proposed controller. As per simulation results, the proposed method was shown to be effective in all of the tested cases. A comparison with particle swarm optimization algorithm (PSO) and Perturb and Observe (P&O) has been provided to assess the efficacy of the proposed method. The results demonstrated the efficacy of the proposed technique by providing a rapid and precise tracking in 0.02 s with high efficiency.