High-Efficiency MPPT Controller Using ANFIS-reference Model For Solar Systems

Abstract

Solar energy is considered as one of the promising renewable sources due to to the availability of sunlight and cleanness performance compared to fossil fuels. The transferred energy from the sun to the Earth changes during the day. So, absorbing the maximum energy by the solar panel and transferring it to the load is essential. Consequently, maximum power point tracking (MPPT) techniques are proposed in numerous research papers. In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller has been introduced. To transfer maximum power to the load, the duty cycle of the two-switch flyback inverter, which has been connected between the solar panel and the load, must be generated with the aid of the proposed ANFIS method. This tracker takes irradiance level and operating temperature as inputs and current at maximum power point as an output. Then Fuzzy controller must be tunned to generate an appropriate duty cycle. For validation, the proposed model was analyzed in different situations by MATLAB-PSIM Co-Simulation, and results show the accuracy and high efficiency of the proposed tracker.