GWO and WOA variable step MPPT algorithms-based PV system output power optimization.

The nonlinear characteristics and low efficiency of photovoltaic (PV) systems remain critical challenges that necessitate advanced solutions. This study proposes two innovative Maximum Power Point Tracking (MPPT) algorithms based on the Whale Optimization Algorithm (WOA) and Grey Wolf Optimization (GWO). The primary advantage of these methods lies in their adaptive step-size optimization, leveraging multiple criteria to determine the optimal step size. A novel fitness function was developed to improve tracking accuracy, minimize ripple, and reduce overshoot. Simulation results demonstrated remarkable improvements, including up to 98% reduction in ripple, 67% reduction in overshoot, and significant improvements in tracking accuracy compared to fixed-step methods. Field validation was conducted using real-world data from the Ain El Melh PV station in Algeria on June 21, 2023. Experimental results confirmed the effectiveness of the proposed methods, with the WOA-based MPPT achieving up to 99% ripple reduction and 40% overshoot reduction under dynamic environmental conditions. A comparative analysis of MPPT algorithms revealed superior performance metrics for the bio-inspired methods. The PO-WOA algorithm achieved the highest efficiency of 98.87% in simulation and 98.94% in real data, surpassing both PO and PO-GWO. It also minimized power loss to 0.56 W in simulation and 0.39 W in real data, demonstrating its optimization capabilities under fluctuating conditions. Although its response time was slightly longer than other methods, at 0.65 s in simulation and 0.48 s in real data, it prioritized stability and precision. These findings underscore the potential of WOA and GWO algorithms to enhance PV system performance, offering robust and efficient solutions for optimizing energy output in both simulation and real-world scenarios.