A novel hybrid particle swarm optimization rat search algorithm for parameter estimation of solar PV and fuel cell model
Manish Kumar Singla, Parag Nijhawan, Amandeep Singh Oberoi
Abstract
Purpose The purpose of the proposed hybrid method aims to increase population efficiency, and a local search is used to further improve the value of the global best solution. An experimental observation suggests that the model’s statistical outcomes are more aligned with the real-time experimental findings. Design/methodology/approach A novel metaheuristic efficient hybrid algorithm, i.e. hybrid particle swarm optimization rat search algorithm, is introduced and applied for parameter extraction of hybrid energy system. This proposed hybrid method rules out the chances of local minima, hence enhancing the precision of the parametric estimation. The parameter extraction and error is calculated for the solar photovoltaic (PV)–fuel cell system using the proposed algorithm. Findings Nonparametric statistical tests are also conducted to indicate the findings of the outcome parameters using various metaheuristic algorithms. The proposed algorithm is better than the rest of the compared algorithms in the study. Originality/value The authors proposed a novel algorithm, and this proposed algorithm is implemented on hybrid solar PV and fuel cell-based system for parameter extraction. The nonparametric test results clearly suggest that the proposed algorithm is far more effective for parameter estimation of the test system.