EFFICIENT POWER HARVESTING FOR FUEL CELLS: DYNAMIC-STEPPED MPPT WITH NEURAL ADAPTIVE WATER CYCLE METHOD

In this research, a new approach based on a dynamic-stepped maximum power point tracking (MPPT) technology and a neural adaptive water cycle (NA-WC) method is proposed to improve the power gathering effectiveness of fuel cells. Although fuel cells have drawn a lot of interest as a clean and effective energy source, maximizing their power harvesting effectiveness is still a major problem. To solve this problem, we present a brand-new dynamic-stepped MPPT method that constantly alters the fuel cell's functioning position to achieve optimum output of power under various environmental circumstances. A 7 kW proton-exchange membrane fuel cell (PEM-FC) that supplied a resistive load through a boost converter created utilizing the suggested MPPT controller was effectively used to study the effectiveness of the suggested NA-WC MPPT. The suggested NA-WC outperforms the traditional MPPT approaches in terms of convergence rate, overshoot, and steady state fluctuations, according to simulation findings obtained by utilizing the Matlab tool. Additionally, the fuel cell's lifespan and effectiveness can both be increased by the suggested controller's current ripple minimization.