A Combined Adaptive Zebra Optimization and Perturb & Observe MPPT Algorithm in PV System under Partial Shading Conditions

Photovoltaic (PV) system faces challenges like partial shading, significantly affecting power output and efficiency. Developing an advanced maximum power point tracking (MPPT) controller is vital for enhancing the performance of PV system. Although extensive research has been conducted on MPPT control under partial shading conditions, focusing on convergence speed and tracking accuracy, the studies on oscillations during the convergence process and the tracking response of the maximum power point (MPP) in changing environments are still insufficient. In this study, we have proposed the adaptive zebra optimization algorithm (AZOA) and combined it with the perturb & observe (P&O) method to specifically address the aforementioned issues of oscillations and tracking response. The newly proposed algorithm, AZOA-P&O, introducing adaptive coefficients and integrating it with P&O, enhances the performance of traditional zebra optimization algorithm (ZOA). Ensuring accurate and rapid convergence to the MPP, AZOA-P&O significantly reduces oscillations during the convergence process and markedly enhances dynamic response to changing environments. The proposed work is carried out in MAT-LAB/Simulink platform, which is compared with perturb & observe (P&O), tasmanian devil optimization (TDO), driving training-based optimization (DTBO) techniques in terms of convergence speed, response speed, and the oscillations during the convergence process. The results significantly demonstrate the superiority of proposed MPPT technology over other MPPT techniques.