A novel global MPPT method based on sooty tern optimization for photovoltaic systems under complex partial shading.

Area of Science:

• Renewable Energy Systems
• Optimization Algorithms
• Photovoltaic Power Generation

Background:

• Global expansion of photovoltaic (PV) systems necessitates advanced Maximum Power Point Tracking (MPPT) algorithms.
• Partial Shading Conditions (PSC) create multiple local maxima, reducing PV energy yield and posing significant tracking challenges.
• Existing MPPT methods often struggle with convergence speed and avoiding local optima in complex shading scenarios.

Purpose of the Study:

• To propose and evaluate a novel MPPT strategy using the bio-inspired Sooty Tern Optimization Algorithm (STOA) for Global Maximum Power Point Tracking (GMPPT).
• To enhance tracking accuracy, convergence speed, and dynamic stability of PV systems under non-uniform irradiance.
• To demonstrate STOA's superiority over conventional optimization algorithms in maximizing energy yield under challenging PSC.

Main Methods:

• Adapted and optimized the Sooty Tern Optimization Algorithm (STOA) for MPPT applications.
• Implemented a simulation framework in MATLAB/Simulink with a 3x3 PV array (3 kW) and a boost converter.
• Tested the STOA-based MPPT against Perturb & Observe (P&O), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) under four distinct PSC scenarios.

Main Results:

• STOA demonstrated superior performance in convergence speed, tracking accuracy, and dynamic stability compared to P&O, PSO, and GWO.
• Under the most challenging PSC (Pattern 4), STOA achieved 99.94% tracking efficiency, 1676 W average power, and 0.5 s response time.
• STOA maintained minimal power oscillations, ensuring stable operation and reduced component wear across all tested shading patterns.

Conclusions:

• The proposed STOA-based GMPPT strategy offers a significant advancement for solar energy harvesting, outperforming benchmark algorithms under complex PSC.
• STOA provides a better balance between exploration and exploitation, accelerating convergence and avoiding local optima in nonlinear PV systems.
• Its computational simplicity and high precision make STOA a scalable, efficient, and reliable solution for real-time embedded applications in distributed solar energy systems.