Combined Neural Network and Fuzzy Control for Maximum Power Point Tracking In Solar PV System

Abstract

The most effective renewable energy in the future among energy. Using photovoltaic (PV) panels is the most effective approach to utilize solar energy for electrical power. The photovoltaic panel that uses solar energy has non-linear voltage-current and voltage-control properties. The control yield from the solar-powered PV board is highest at a certain voltage point. The maximum power point voltage is the voltage at which the PV board produces the most power. The construction of the solar-powered PV board and a description of its features have been presented. In order to acquire the highest control point voltage, the difference between the real Maximum Power Point Tracking (MPPT) voltage and the MPPT voltage deduced from the Artificial Neural Network (ANN) arrangement is displayed, and it is successfully accepted. The result appears to be an exact match for the accuracy of ANN. The PV board's highest control point can be successfully and accurately tracked using the ANN display that was obtained. From the various results obtained, it becomes obvious that the suggested computation proves to be considerably simpler in following the PV board's most extreme control point. The reaction time is drastically reduced when the proposed control approach is used since the PV voltage closely tracks the highest control point voltage. Additionally, the proposed strategy's precision is incredibly logical. In every irradiance and temperature scenario, the control framework performs well.