Parameter Estimation of PV Solar Cells and Modules using Metaheuristic Optimization Algorithm

Abstract

Photovoltaic (PV) solar cells and modules are crucial components of renewable energy systems, necessitating accurate parameter estimation for optimal performance and efficiency. This paper proposes the utilization of the Grasshopper Optimization Algorithm (GOA) for parameter estimation in PV solar cells and modules. The proposed methodology aims to enhance the accuracy and efficiency of parameter estimation by leveraging the unique search mechanism of the GOA, which mimics the foraging behavior of grasshoppers in nature. Through iterative optimization, the GOA efficiently explores the solution space to identify optimal parameters that best fit experimental data, such as current-voltage (IV) and power-voltage (PV) characteristics. The paper provides a comprehensive overview of the parameter estimation process, detailing the formulation of the objective function to minimize the error between experimental and simulated data. Furthermore, it discusses the implementation of the GOA algorithm and its integration with mathematical models of PV solar cells and modules. To validate the effectiveness of the proposed approach, experimental data from real-world PV systems are utilized. Comparative analyses with other optimization algorithms demonstrate the superior performance of the GOA in terms of convergence speed and accuracy in parameter estimation. The results indicate that the proposed methodology offers a robust and efficient solution for parameter estimation in PV solar cells and modules, thereby facilitating the design, optimization, and maintenance of photovoltaic systems. The integration of the GOA algorithm contributes to advancing the state-of-the-art in renewable energy technologies, promoting the widespread adoption of solar power generation for sustainable development. The proposed algorithm significantly outperforms all competitors in SMD, with WOA being the closest but still 26.1% worse. While GWO performs well in DDM, it still lags behind the suggested method by 31.7%. Although achieving comparable results to COA in PV, the proposed algorithm maintains an edge with COA trailing by 4.2%.