A New Approach for Solar Photovoltaic Parameter Extraction Using Metaheuristic Algorithms From Manufacturer Datasheet

Estimating the parameters of solar photovoltaic (PV) panels is crucial for effectively managing operations in solar-based microgrids. Various techniques have been developed for this purpose, and one accurate approach is solar cell modeling using metaheuristic algorithms from current–voltage ( ${I}$ – ${V}$ ) data of the PV panel. However, this method relies on experimental datasets, which may not be readily available for most industrial PV panels. Hence, this research proposes a new technique for estimating the parameters of different types of PV modules using only manufacturer datasheets. Additionally, three metaheuristic optimization techniques, namely, particle swarm optimization (PSO), artificial bee colony (ABC) optimization, and Harris Hawks optimization (HHO), are investigated for solving this problem. The obtained results using these optimizers indicate that PSO mostly outperforms other algorithms, in terms of accuracy, while demonstrating faster computation. The proposed method is evaluated for three different PV units. Under 1000W/m2 of irradiance and a specified temperature, the method has been validated with available experimental datasets. Furthermore, a comparative analysis with some other existing methods in the literature reveals the model’s competitiveness despite not relying on experimental datasets. Also, an uncertainty analysis for the extracted parameters has shown that the obtained results are reliable enough to predict the actual dynamics of PV units. This study holds significance for other research on the basis of PV panel parameters, managing commercial PV power plant operation with with maximum power point tracking controller, etc.