Enhanced MPP Tracking in Partial Shading Conditions for Solar PV Systems: A Metaheuristic Approach Utilizing Projectile Search Algorithm

This project focuses on the solar Photovoltaic (PV) systems to function effectively amid dynamic conditions, it is imperative to achieve efficient power harvesting. Given the escalating demand for energy, the application of solar PV technology for electricity generation must be optimized to ensure optimal performance and cost-effectiveness. Maintaining an ample supply of power at the lowest cost is crucial in this regard. Partial Shading Conditions (PSCs) significantly reduce power transfer efficiency in solar PV systems, potentially leading to the formation of hotspots within the solar array. While the insertion of bypass diodes can address this issue, it often results in multiple power peaks on the Power vs Voltage (P-V) curve characteristics, complicating the process of maximum power tracking. To overcome this challenge and alleviate the computational load on the microcontroller, the application of metaheuristic techniques for Maximum Power Point Tracking (MPPT) proves beneficial. However, due to the distinctive operational characteristics of metaheuristic algorithms, continuous research in this domain is essential. Addressing the need for effective Maximum Power Point (MPP) capture in diverse partial shading scenarios, this study introduces a novel MPPT technique based on the Projectile Search Algorithm (PSA). The PSA, inspired by the projectile motion of physical objects, is a metaheuristic optimization algorithm tailored for solving optimization problems by simulating projectile motion within a search space to identify optimal solutions. To assess the performance of the proposed PSA-based approach, comparisons are made with Jaya, Cuckoo Search, Particle Swarm Optimization (PSO) and Perturb and Observe (P&O) algorithms. MATLAB/Simulink software is utilized for evaluation.

Keywords: Projectile search algorithm (PSA), maximum power point tracking, partial shading condition, metaheuristic algorithms, photovoltaic (PV), optimization.