Design and implementation of adaptive pufferfish optimization algorithm based efficient MPPT and DC-DC boost converter for agriculture applications under partial shading conditions

In today's world, the efficient utilization of renewable energy sources is necessary to meet the growing need for sustainable agriculture. The purpose of this work is to maximize the performance of solar water pumps in agricultural applications using the maximum power point tracking (MPPT) method through the use of the adaptive puffer optimization algorithm (APOA). The APOA is a potent mechanism that can regulate the power-voltage curve of solar panels to maximize energy recovery. The system has a water pump, solar panel, DC-DC converter, and an APOA-based MPPT controller that continuously modifies the duty cycle of the converter. An initial collection of viable solutions is sent to the APOA, which iteratively improves these solutions in light of the observed optimal result. The algorithm effectively explores and utilizes the search space to converge to the optimal operating point by imitating the inflating and deflation behaviors of pufferfish. According to the simulation findings, the APOA-based MPPT controller tracked the maximum power point under various solar irradiation conditions with good speed and accuracy. The Adaptive Pufferfish Optimization Algorithm provides a powerful and efficient framework for MPPT in PV-powered agricultural water pump systems, ensuring optimal performance and contributing to the sustainability of modern agriculture. Important performance indicators, such as tracking accuracy, stability, and convergence speed, were assessed and compared with conventional MPPT techniques. Because APOA-based systems offer a higher tracking efficiency of 99.67%, quantum efficiency of 99.05%, and faster convergence speeds of 10.23 s, they guarantee a steady and dependable water supply for agricultural irrigation.