Abstract
As the deployment of photovoltaic (PV) systems continues to expand globally, the need for robust and highly efficient Maximum Power Point Tracking (MPPT) algorithms becomes increasingly critical, particularly under complex Partial Shading Conditions (PSC) where multiple local maxima can significantly reduce energy yield. This paper proposes a novel MPPT strategy based on the bio-inspired Sooty Tern Optimization Algorithm (STOA) for Global Maximum Power Point Tracking (GMPPT) in PV arrays subjected to non-uniform irradiance. The STOA algorithm, originally developed for solving complex multimodal optimization problems, is here adapted and optimized for MPPT tasks, demonstrating superior capabilities in terms of convergence speed, tracking accuracy, and dynamic stability. Unlike conventional optimizations like PSO and GA, STOA offers a much better balance between exploration and exploitation, thus accelerating convergence and minimizing the likelihood of being trapped in local optima. These advantages prove beneficial in nonlinear, PV-shading complicated systems. A comprehensive simulation framework was implemented in MATLAB/Simulink, employing a 3 × 3 PV array (3 kW capacity) and a boost converter to test the proposed method across four shading scenarios, including highly irregular and dynamic patterns. Performance evaluation against benchmark algorithms-Perturb & Observe (P&O), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO)-revealed that STOA consistently outperformed its counterparts. Specifically, under the most challenging PSC scenario (Pattern 4), the proposed method achieved a tracking efficiency of 99.94%, with an average power output of 1676 W and a response time of 0.5 s, outperforming PSO (97.1%, 1630 W), GWO (94.2%, 1580 W), and P&O (56.5%, 950 W). Moreover, STOA maintained minimal power oscillations across all test patterns, ensuring stable operation and reduced wear on system components. Its computational simplicity and high precision make it particularly well-suited for real-time embedded applications in distributed solar energy systems. The proposed STOA-based MPPT framework represents a significant advancement in global optimization-based solar energy harvesting and provides a scalable, efficient, and reliable solution for maximizing PV system performance under real-world operating conditions.