A transfer function modelling using system identification for air-cooling photovoltaic system

Mathematical modelling is essential in comprehending, optimising, and administering air-cooling photovoltaic (PV) systems by considering factors like temperature, irradiance, and module characteristics. Mathematical modelling allows for informed decision-making, optimisation, and risk management in designing, operating, and maintaining air-cooling PV systems. This study creates mathematical models to identify an air-cooling PV system and anticipate the PV module’s output performance based on the solar irradiance received. The air-cooling PV system is modelled using the system identification toolbox, which relies on experiment data. The modelling process utilises a black-box approach, eliminating the necessity for internal parameter knowledge. The transfer function estimation method was selected as the best non-linear model due to its superior fit percentage. Prior to the installation of the air-cooling system, the data-driven analysis produced a continuous-time transfer function with an accuracy of 90% for the PV module model, whereas the air-cooling PV system model obtained an accuracy of 94.3%. The validity of the acquired models was assessed using Simulink by employing multiple levels of PSH. The model exhibits a failure rate of less than 10% in predicting inequality. The validation results for the PV module model were 90.1% and 90.8% for high, moderate, and low PSH, respectively. Similarly, the air-cooling PV system model got validation results of 91.7%, 93.2%, and 91.5%, while the mean output voltage increased by 10.8%, 17.5%, and 15.3%. Consequently, a continuous-time transfer function model is created, which will be utilised for developing and tuning controllers in future research.

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