Radial movement optimization based parameter extraction of double diode model of solar photovoltaic cell

Abstract

A new swarm-based stochastic radial movement optimization (RMO) algorithm is proposed for extracting unknown solar photovoltaic (PV) cell parameters. The explicit modelling of a solar PV cell is shown to be very influential in the performance assessment of maximum power point tracking methods. The performance of the Single-Diode Model (SDM) and Double-Diode Model (DDM) of a Kyocera KC200GT 200 W panel was verified and validated under different test conditions in the MATLAB Simulink environment. The objective of this study was to validate the accuracy of solar PV cell modelling and determine the best optimization algorithm among the RMO, particle swarm optimization (PSO), and differential evolution technique (DET). The RMO-based I-V and P-V curves were compared with those obtained by the DET and PSO methods. Additionally, statistical and error analyses were carried out to calculate the relative error (RE), individual absolute error (IAE), and root mean square error (RMSE) of the proposed method for better analysis. With the RMO method, the IAE and RE for the DDM of the solar PV cell was 0.0224 and 0.0509, respectively. For the DDM, the fitness function value of the RMO was 3.01E−4. The performance of the RMO method was superior to that of the PSO and DET methods based on curve fitting for the SDM, DDM, and datasheet values. Curve fitting with the RMO strongly fitted the datasheet curve, which resembled the RMO curve, and is possibly a suitable optimization approach for extracting the parameters of the DDM of the solar PV cell.