Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions

Solar energy is one of the most promising renewable resources. Over last few decades, photovoltaic (PV) systems have grown in popularity. Since maximum power point (MPP) a solar system changes with environmental circumstances, tracking (MPPT) technique required to get out system. Various MPPT techniques based on classical and artificial intelligence (AI) methodologies been proposed literature so far. In this paper, we aim provide thorough comparative analysis widely used algorithms AI. The discussed are fuzzy logic (FL), neural networks (ANN), suggested hybrid approach ANN-fuzzy. designed controllers evaluated same PV system, which consists module, DC-DC boost converter, DC load, under weather profile. Using MATLAB/Simulink simulation tool, accuracy, response time, overshoot, steady-state ripple each method tested different conditions. results show that ANN-fuzzy tactic outperforms both FL ANN correctly successfully diverse atmospheric