Maximum Power Point Tracking Technique based on a Neural-Fuzzy Approach for Stand-alone Photovoltaic System

Maximum Power Point Tracking (MPPT) technique is a vital element for improving the efficiency, stability and reliability of a photovoltaic system. Adaptive Neural Fuzzy Interference System (ANFIS) is used to design the MPPT due to its fast-tracking speed to reach the steady-state and low oscillation. However, it requires a precise data to develop an optimal ANFIS-MPPT controller. In this paper, the ANFIS-MPPT controller for a stand-alone PV system is designed using a large experimental training dataset. The dataset is recorded from a PV system which is installed at Brunel University London, United Kingdom. A MATLAB/Simulink model of a stand-alone PV system is developed to evaluate the proposed method. The results show that the proposed controller performs well under various climatic conditions.