In the case of static PV modeling (single, double, and three diode models), the load variation and switching operation of the inverter and DC/DC converter stages are not considered. Therefore, another type of PV model named integer order dynamic PV model (IOM) has been introduced, which is the most efficient and accurate model to handle the static models' aforementioned drawbacks. That is why the dynamic model is the preferable one for the design of the grid-connected PV systems. Recently, the theory of fractional calculus has been employed to reinforce the efficiency and flexibility of IOM. As a result, the fractional-order dynamic PV model (FOM) has been introduced as the latest trend in tackling the PV models' dynamic behavior. The accuracy of the dynamic PV models is mainly influenced by obtaining their parameters under different operating conditions. The manufacturers usually undefine the models’ parameters. Therefore, it is crucial to identify these parameters accurately with minimum execution time using the experimental load current–time (I-T) curve [1]-[2]-[3].
[1]AbdelAty AM, Radwan AG, Elwakil AS, Psychalinos C. Transient and steady-state response of a fractional-order dynamic PV model under different loads. J Circ Syst Comput 2018;27(02):1850023. https://doi.org/10.1142/s0218126618500238
[2] Yousri, D., Allam, D., Eteiba, M.B. and Suganthan, P.N., 2019. Static and dynamic photovoltaic models’ parameters identification using Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants. Energy conversion and management, 182, pp.546-563.
[3] Enhanced Marine Predators Algorithm for identifying static and dynamic Photovoltaic models parameters March 2021 Energy Conversion and Management ( In proofing).
Note: To implement the code for optimizing the fractional order model. The user should click on fomcon-1.21b right click and select add to the path ( then select folders and subfolders) to let all the inside files are readable. Then use main to implement the optimization process