Design and implementation of ANFIS-reference model controller based MPPT using FPGA for photovoltaic system

The aim of this work is to demonstrate the usefulness of Adaptive Neuro Fuzzy Inference System (ANFIS) for tracking Maximum Power Point (MPP) in stand-alone photovoltaic system. Maximum Power Point Tracking (MPPT) is one of approaches which boost efficiency of PhotoVoltaic (PV) cells by the load matching between the PV cells and the load. The key problem is that maximum power is not achieved because PV cells power is affected by weather conditions such as the solar irradiation and the temperature, thus, the MPP is changed during daylight hours and year seasons. Therefore, it is necessary to design an appropriate controller based on one of techniques to track MPP. These techniques are based on true or estimated searching mechanism to MPP. True searching mechanism based techniques like incremental conductance method and perturb and observe method are efficient but they are less stable, more oscillatory about MPP and sensitive to a high frequency noise. Generally, estimated searching mechanism based techniques like constant voltage method and fractional open circuit voltage method are less efficient, but they are stable and no sensitive to a high frequency noise. In this paper, the ANFIS-reference model method in addition to the incremental conductance method and constant voltage method have been studied, designed and implemented using Field Programmable Gate Array (FPGA) board to compare the performance of each method. The proposed ANFIS-reference model controller is efficient since it has been trained offline using Matlab tool with practical data sets. Based on our knowledge, this paper is the first paper which introduces practical implementation of ANFIS-reference model based MPPT for photovoltaic system using FPGA board. The results reveal that the ANFIS-reference model controller has more efficient and better dynamic response than the incremental conductance method and constant voltage method.