Maximizing energy harvest from photovoltaic systems remains a fundamental engineering challenge, particularly when environmental conditions fluctuate. Researchers have developed an advanced Maximum Power Point Tracking (MPPT) controller that combines three complementary optimization algorithms to enhance performance across diverse operating conditions.
The proposed tri-meta hybrid controller integrates the Dandelion Optimizer Algorithm for broad exploration of operating points, the Crow Search Algorithm for refining promising regions using memory-based search logic, and the Whale Optimization Algorithm for fine-tuned exploitation near the true maximum power point. This sequential structure leverages each algorithm's strengths: exploration capability, regional refinement, and precision convergence.
Simulation testing under constant irradiance (1000 W/m² at 25°C) yielded 10,141 W output with 99.19% tracking efficiency, outperforming individual algorithms and dual-algorithm combinations. Under variable irradiance and temperature conditions, the hybrid controller delivered 6,549 W—a 3.73% gain over the best single algorithm. Most significantly, during partial shading scenarios where panel segments receive uneven sunlight, the hybrid controller achieved 8.86% improvement over baseline performance, a critical metric for real-world installations where shading from buildings and vegetation is unavoidable.
The computational complexity analysis confirms that hybridization adds modest overhead while delivering substantial efficiency gains. MATLAB/Simulink validation demonstrates faster convergence speeds and improved robustness compared to conventional MPPT approaches such as perturb-and-observe or incremental conductance methods.
These results validate hybrid metaheuristic optimization for PV system design. As solar deployment accelerates globally, controllers maximizing energy capture under real-world conditions directly reduce system costs and accelerate return on investment. The approach provides a practical pathway for integrating advanced optimization into next-generation PV inverters and charge controllers.
