Abstract:
Recently, building an accurate mathematical model with the help of the experimentally measured data of solar cells and Photovoltaic (PV) modules, as a tool for simulation and performance evaluation
of the PV systems, has attracted the attention of many researchers. In this work, Coyote Optimization
Algorithm (COA) has been applied for extracting the unknown parameters involved in various models for
the solar cell and PV modules, namely single diode model, double diode model, and three diode model. The
choice of COA algorithm for such an application is made because of its good tracking characteristics and
the balance creation between the exploration and exploitation phases. Additionally, it has only two control
parameters and such a feature makes it very simple in application. The Root Mean Square Error (RMSE)
value between the data based on the optimized parameters for each model and those based on the measured
data of the solar cell and PV modules is adopted as the objective function. Parameters’ estimation for various
types of PV modules (mono-crystalline, thin-film, and multi-crystalline) under different operating scenarios
such as a change in intensity of solar radiation and cell temperature is studied. Furthermore, a comprehensive
statistical study has been performed to validate the accurateness and stability of the applied COA as a
competitor to other optimization algorithms in the optimal design of PV module parameters. Simulation
results, as well as the statistical measurement, validate the superiority and the reliability of the COA algorithm
not only for parameter extraction of different PV modules but also under different operating scenarios. With
the COA, precise PV models have been established with acceptable RMSE of 7.7547×10−4
, 7.64801×10−4
,
and 7.59756 × 10−4
for SDM, DDM, and TDM respectively considering R.T.C. France solar cell.