Solar energy is recognized as one of the most popular renewable energy sources due to its availability around the world. Because of the intermittent generation of solar power, it is vital to extract the maximum power available from solar panels all day long. For this purpose, Maximum Power Point Tracking (MPPT) techniques are investigated in several papers. In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPPT controller with just one current sensor has been proposed to achieve that intent. To extract the maximum power from the solar panel, a DC-AC isolated two-switch flyback inverter is connected between the solar panel and the load. To transfer the maximum power to the load, the duty cycle of this converter must be generated with the aid of the proposed ANFIS method. This ANFIS structure takes the voltage and the current of the photovoltaic (PV) module as inputs and generates the maximum power point voltage as the output. Then, with the aid of a fuzzy controller, an appropriate duty cycle will be generated to control the switches of the two-switch flyback inverter. To validate this proposed system, the MATLAB-PSIM co-simulation was done. The results prove that the proposed MPPT controller for a two-switch flyback inverter can track the maximum power under different atmospheric conditions (irradiance and temperature), as well as other advantages from our investigation.
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