Hybrid cuckoo search-gorilla troops optimizer for optimal parameter estimation in photovoltaic modules

Photovoltaic (PV) power plants have recently attracted the attention of researchers and power system developers. One of the challenges of PV systems is ensuring an accurate mathematical model of the PV module . Accurate modeling of the behavior of these modules is indispensable for reliable system evaluation, analysis, design, development, and optimization of the performance and efficiency of photovoltaic systems. This paper presents the optimal estimation of the parameters of a mathematical model for a PV cell and module. Moreover, a new hybrid cuckoo search-gorilla troop optimization CS-GTO algorithm is proposed. The performance of the proposed hybrid CS-GTO algorithm is tested and evaluated on standard 23 benchmark functions and several data sets of commercial PV cells and modules. The obtained results were compared with other optimization algorithms mentioned in the literature, the comparative study conclusively demonstrates that the hybrid CS-GTO algorithm consistently outperforms other methods in terms of accuracy and convergence speed towards the global optimal solution, making it a robust tool for a wide range of optimization problems.

• metaheuristic optimization
• solar cell modeling
• hybrid metaheuristic
• benchmark function optimization
• convergence rate

• 7 — طاقة نظيفة وبأسعار معقولة

• Metaheuristic Optimization Algorithms Research
• Photovoltaic System Optimization Techniques
• Solar Radiation and Photovoltaics
• metaheuristic optimization
• solar cell modeling
• hybrid metaheuristic
• benchmark function optimization
• convergence rate
• Artificial Intelligence
• Renewable Energy, Sustainability and the Environment
• Computer Science
• Energy