A photovoltaic maximum power point tracking strategy based on the IRBMO-VP&O algorithm

Under partial shading conditions (PSC), the Power–Voltage (P–V) output curve of a photovoltaic (PV) array shows multiple peaks. To address this, this paper introduces a hybrid maximum power point tracking (MPPT) strategy called the improved red-billed blue magpie optimization algorithm combined with a variable-step perturb and observe algorithm (IRBMO-VP&O) of the exponential decay. This strategy merges an improved red-billed blue magpie optimization (IRBMO) algorithm with a variable-step perturb and observe (VP&O) method. It incorporates Lévy flight and an individual diversity mechanism to boost its global search ability. Additionally, it uses adaptive step-size fine-tuning for better tracking accuracy and a restart mechanism triggered by sudden power changes, enhancing its adaptability to dynamic environments. MATLAB/Simulink simulations compare the proposed algorithm with GWO, PSO, RIME, SSA, IGWO-VINC, PSO-P&O, RBMO, IRBMO, INC, and P&O. Under static shading conditions, IRBMO-VP&O outperformed others by reducing average convergence time by 52.99% (to 0.042–0.067 s) and increasing tracking accuracy by 4.06% (to 91.785–99.988%) compared to eight other algorithms. Under dynamic conditions, it achieved an average convergence time of 0.055 s and tracking accuracy above 99.986%, consistently locking onto the global maximum power point (GMPP) without local optima trapping.