A new grey wolf optimization-based parameter estimation technique of solar photovoltaic

Firstly, this paper develops a non-linear least square objective function in terms of three unknown parameters of solar photovoltaic, namely series resistance, shunt resistance, and ideality factor. Then, grey wolf optimization is used to estimate these unknown parameters. The remaining unknown parameters, namely light-induced current and diode dark saturation current, are estimated analytically. Furthermore, solar photovoltaic modules of mono-, poly-crystalline, and thin-film technologies are considered to check the accuracy and effectiveness of the pro-posed technique. Secondly, the outcomes of the proposed method are compared with the numerical, analytical, metaheuristics, and different existing hybrid methods. Additionally, through the various benchmarks, such as model error, absolute relative maximum power error, root mean square error, mean relative absolute error, and mean absolute error, the performance of the proposed method is evaluated. The study reveals that the model error and absolute relative maximum power error are zero in case of the proposed method. Further, among all tested methods, the minimum value of 0.21, 0.1, and 0.046 of the root mean square error, mean relative absolute error, and mean absolute error, respectively, is estimated for module KC200GT via proposed method. Thus, the study reveals that the proposed technique is more accurate than the existing methods.