HYBRID SALP SWARM-GAINING AND SHARING KNOWLEDGE (SSA-GSK) METAHEURISTIC ALGORITHM FOR EXTRACTING PHOTOVOLTAIC CELL PARAMETERS

Maximizing the energy output of photovoltaic cells depends firstly on accurately estimating their parameters using one of the three models.This parameter estimation is a multi-variable, nonlinear, and multi-modal problem.Developing more accurate and advanced solutions for this remains a challenging research problem.This study aims to develop a hybrid algorithm to precisely extract the parameters from photovoltaic cells and improve the results of the Salp Swarm Algorithm (SSA).To achieve this goal, a hybridization approach is implemented by combining two metaheuristic algorithms, SSA and Gaining and Sharing Knowledge (GSK), to extract parameters.The hybrid SSA-GSK algorithm was tested on RTC France photovoltaic cells, amorphous silicon photovoltaic cells (aSi), and STM6-40 using single and double diode models.The performance of the SSA-GSK hybrid algorithm is comparatively analyzed using the root mean square error (RMSE) and statistical analysis.RMSE was calculated using a hybrid SSA-GSK algorithm and obtained as 0.00261 for RTC SDM and 0.00844439 for RTC DDM, 6.361410 -05 for aSi SDM and 9.493610 -05 for aSi DDM, 2.034910 -3 for STM6-40/36 SDM and, 2.074010 -3 for STM6-40/36 DDM.The results demonstrated that the hybrid SSA-GSK algorithm achieved a lower rate of RMSE in comparison to SAA and other literature-reported algorithms.The algorithm was more proficient in search space, leading to superior optimization results in lesser execution time after hybridization.The proposed algorithm increases the probability of generating high-quality solutions and outperforms some other optimization algorithms while maintaining ease of implementation.