Assessing Maximum Power Point Tracking Intelligent Techniques on a PV System with a Buck–Boost Converter

Classic and intelligent techniques aim to locate track the maximum power point of photovoltaic (PV) systems, such as perturb observe (P&O), fuzzy logic (FL), artificial neural networks (ANNs), adaptive neuro-fuzzy inference systems (ANFISs). This paper proposes compares three algorithms for tracking (MPPT) control, specifically fuzzy, ANN, ANFIS. The modeling a single-diode equivalent circuit-based 3 kWp PV plant was developed validated achieve this purpose. Then, MPPT were designed applied control buck–boost converter’s switching device plant. All methods use ambient conditions input variables: solar irradiance temperature. proposed methodology comprises study dynamic response generated it compared classic P&O technique under varying conditions. We observed that outperformed method in speed, accuracy, reducing oscillation around (MPP). ANN better algorithm energy gain, managing recover up 9.9% power.