Optimal Parameter Identification and Extraction of the Solar Module Using Empirical and Quasi-Newton Optimisation Methods.

Abstract

 Parameter identification and extraction of a typical solar module is critical for its effective computation, performance analysis, and optimum power point tracking (OPPT) of the entire photovoltaic (PV) cell working system. The determination or identification of Photovoltaic (PV) model parameters is a tough task mostly when a single or double model is involved. This is due to the nonlinear performance pattern of the current, voltage, and power relationship when in use. Therefore, this paper presents a combination of a laboratory experimental study and an innovative numerical solution approach using the Quasi-Newton method to program and identify the parameters of the single PV model. The peculiar characteristics of the PV cell model panel were studied via the current-voltage (IV) curvatures. For comparative analysis purposes, two more numerical optimization techniques, namely Trust region and Levenberg- Marquardt were also applied to determine the solar cell model parameters in correspondence with three different experimental data acquired in the laboratory. Furthermore, to determine fitting accuracies of the engaged three optimization techniques, three key performance indicators involving the Root Mean Square Error (RMSE), Standard deviation, and Mean Absolute Error (MAE) have been provided. From the results, the proposed Quasi-Newton yields the most preferred fitting performance over the benchmarked Trust region and Levenberg- Marquardt methods by attaining RMSE, MAE, and STD values of 0.00150, 0.00576, and 0.00094 with data 1; 0.00172, 0.00443, and 0.00142 with data 2; 0.00570, 0.00184 and 0.00200 with data 3, respectively. The explored hybrid-based approach can also be extended to identify the double or multi-cell parametric models of the solar cell.