Parameter Identification of PV Panels Through Metaheuristic Algorithms: A Comparative Study
in: Springer Tracts in Additive Manufacturing, Springer Nature, pp.183-192, 2026
- Publication Type: Book Chapter / Chapter Research Book
- Publication Date: 2026
- Doi Number: 10.1007/978-3-032-21869-8_13
- Publisher: Springer Nature
- Page Numbers: pp.183-192
- Keywords: Manta Ray Foraging Optimization, Metaheuristic algorithm, Photovoltaic
- Yıldız Technical University Affiliated: Yes
Abstract
A photovoltaic (PV) system is recognized as a key renewable energy source that significantly contributes to electricity generation and reduces environmental pollution. Accurately predicting the key parameters that define the nonlinear electrical equation of a photovoltaic panel is essential for effectively simulating photovoltaic systems. In most cases, determining the unknown parameters of PV using the advantages of the one-diode model is sufficient to validate dynamic simulation analyses. In this study, Manta Ray Foraging Optimization (MRFO) is used to estimate the unknown parameters of photovoltaic (PV) modules and is compared with various other metaheuristic algorithms documented in the literature. The MRFO algorithm is a reliable optimization method that mimics the foraging behavior of intelligent manta rays. It is designed to extract five unknown model parameters of single-diode PV cells and modules. The results from the MRFO optimization are compared with those obtained from other metaheuristic algorithms reported in existing literature. A comprehensive comparative analysis is conducted to evaluate the effectiveness of the MRFO algorithm in the parameter extraction process for solar modules. The predictive results demonstrate that metaheuristic algorithms can effectively address the parameter estimation challenges of PV modules, yielding accurate model predictions without requiring excessive computational resources. It can be concluded that MRFO is more promising than the other compared algorithms.