A Flying Squirrel Search Optimization for MPPT under Partial Shaded Photovoltaic System

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 (PO), particle swarm optimization (PSO), and gray wolf optimization (GWO).