Photovoltaic models parameter estimation via an enhanced Rao-1 algorithm

The accuracy of unknown parameters determines the accuracy of photovoltaic (PV) models that occupy an important position in the PV power generation system. Due to the complexity of the equation equivalent of PV models, estimating the parameters of the PV model is still an arduous task. In order to accurately and reliably estimate the unknown parameters in PV models, in this paper, an enhanced Rao-1 algorithm is proposed. The main point of enhancement lies in i) a repaired evolution operator is presented; ii) to prevent the Rao-1 algorithm from falling into a local optimum, a new evolution operator is developed; iii) in order to enable population size to change adaptively with the evolutionary process, the population size linear reduction strategy is employed. To verify the validity of ERao-1 algorithm, we embark a study on parameter estimation of three different PV models. Experimental results show that the proposed ERao-1 algorithm performs better than existing parameter estimation algorithms in terms of the accuracy and reliability, especially for the double diode model with RMSE 9.8248E-04, three diode model with RMSE 9.8257E-04 for the R.T.C France silicon cell, and 2.4251E-03 for the three diode model of Photowatt- PWP201 cell. In addition, the fitting curve of the simulated data and the measured data also shows the accuracy of the estimated parameters.