Research on DOA-GCRA-Based MPPT Optimization and Grid-Connected Control of Photovoltaic Systems Under Partial Shading Conditions

Partial shading conditions (PSCs) produce multiple peaks in photovoltaic (PV) power–voltage curves, which can trap maximum power point tracking (MPPT) controllers at local optima. This study introduces a Dream Optimization Algorithm (DOA)-derived disturbance into a Greater Cane Rat Algorithm (GCRA)-inspired guided update. Each candidate represents a bounded scalar PV-voltage reference, and measured PV power defines its fitness. Normal-density-based irradiance profiles provide three non-uniform shading scenarios for evaluation in a MATLAB/Simulink grid-connected PV model. DOA-GCRA, particle swarm optimization (PSO), and the mayfly algorithm (MA) are compared using the same reported population size, iteration limit, and voltage-search interval. DOA-GCRA gives settling times of 0.0459 s, 0.0541 s, and 0.0705 s for σ = 0.5, 1, and 2, respectively. In the reported ablation runs, these times are 45.49–55.49% shorter than those of GCRA and 63.72–69.57% shorter than those of DOA. An additional irradiance-step case demonstrates tracking of a changed operating point. The results support the performance of the hybrid update in the tested cases; repeated-run uncertainty, the harmonic-analysis settings, and hardware performance remain outside the verified scope of these simulations.