Abstract
Large-scale solar photovoltaic (PV) systems encounter unpredictable partial shaded conditions (PSCs). PSC, causing multiple peaks in the power-voltage ( P- V) char- acteristics, potentially downgrades the performance of the PV system. However, the PV system should be operated at a global maximum power point (GMPP) for its efficient utilization. For the tracking of GMPP, a scheme based on flying squirrel search optimization (FSSO) is proposed in this work. For an effective adoption with much-reduced convergence time, the original FSSO is modified to update the squirrel position without the presence of a predator. An experimental investigation of the proposed scheme is carried out employing a quasi-Z-source converter for the extraction of maximum power under PSC. The proposed scheme yields higher tracking efficiency, nonoscillatory steady-state response, and lower transients. Simulation and experimental investigations under various shading patterns indicate that the proposed strategy outperforms other popular maximum power point tracking (MPPT) strategies based on perturb & observe (P&O), particle swarm optimization (PSO), and gray wolf optimization (GWO).
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