Hybrid walrus optimization algorithm techniques for optimized parameter estimation in single, double, and triple diode solar cell models

Abstract

Among all renewable energy sources, solar energy holds the greatest potential for electricity production. This transformation from solar to electrical energy is facilitated by solar cells, typically modeled using single-diode, double-diode, and triple-diode representations. In this study, we evaluate the effectiveness of the Walrus Optimization Algorithm (WOA) for estimating the parameters of these models. Furthermore, we introduce three innovative hybrid variants of WOA that incorporate chaotic sequences, adaptive modifications, and integration with the Simulated Annealing (SA) algorithm, thereby enhancing the parameter estimation process. Our research was conducted on two well-documented types of solar cells/modules, with additional tests on the performance of these algorithms on a solar panel under varying insolation and temperature conditions. The results underscore the superior efficiency, accuracy, and practicality of the hybrid algorithms, particularly the variant augmented with chaotic sequences, over traditional parameter estimation methods in solar cell technologies. This paper highlights significant advancements in algorithmic approaches, paving the way for more precise and reliable solar energy technologies.