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Due to its effectiveness and affordability, solar Photovoltaic (PV) systems are now being employed more and more in many application systems. The biggest challenge is still getting the most energy out from PV panels in a variety of climatic situations. In order to achieve the maximum energy production, several optimization-based MPPT controlling techniques have been developed in traditional works. However, it has significant issues with low convergence, computational complexity, a long time needed to find the best solution, and inefficiency. As a result, the goal of this research is to implement a novel and hybrid optimization technique, named as, Prairie Dog Optimization Algorithm (PDOA) + Mongoose Optimization (MO) for extracting the highest possible energy from PV panels. The original contribution of this work is to incorporate the functions of two different and recently developed optimization techniques for MPPT controlling. For this purpose, the PDOA and MO algorithms are taken into account, and the hybridized PDOA+MO provides the benefits of fast convergence, increased tracking efficiency, and reduced tracking time. The high gain Luo DC-DC converter is also used to increase PV's output power and voltage while minimising switching and conduction losses. Consequently, the voltage source inverter is used to suppress the level of harmonics for providing the quality improved AC output to the grid system. The suggested PDOA+MO algorithm's effectiveness and power tracking performance are validated through a thorough simulation analysis using a variety of parameters.

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