Adaptive Hybrid MPPT for Photovoltaic Systems: Performance Enhancement Under Dynamic Conditions

Optimizing energy conversion in photovoltaic (PV) systems is crucial for maximizing energy conversion efficiency and ensuring reliable operation. Achieving this requires that the PV array consistently operates at the Global Maximum Power Point (GMPP). Conventional Maximum Power Point Tracking (MPPT) algorithms, such as Perturb and Observe (P&O) and Incremental Conductance (INC), perform effectively under uniform irradiance but fail to track the GMPP under partial shading conditions (PSCs), resulting in energy losses and degraded system efficiency. To overcome this limitation, this paper proposes a hybrid MPPT method that integrates the Crayfish Optimization Algorithm (COA), a bio-inspired metaheuristic, with the P&O technique. The proposed approach combines the global exploration ability of COA with the fast convergence of P&O to ensure accurate and stable GMPP identification. The algorithm is validated under multiple irradiance patterns and benchmarked against established MPPT methods, including voltage-source and current-source region detection, Improved Variable Step Perturb and Observe and Global Scanning (VSPO&GS), and a hybrid Particle Swarm Optimization (PSO)-P&O method. Simulation studies performed in MATLAB/Simulink demonstrate that the proposed technique achieves higher accuracy, faster convergence, and enhanced robustness under PSCs. Results show that the proposed method reliably identifies the global peak, limits steady-state oscillations to below 1%, restricts maximum overshoot to 0.5%, and achieves the fastest settling time, stabilizing at the new power point significantly faster following major step changes, thereby enhancing overall PV system performance.