Parameter Identification of Photovoltaic Models Using an Improved Differential Evolution With Selective Perturbation

Appropriate parameter settings of the photovoltaic (PV) model play a crucial role in accurately predicting the I-V behavior of actual PV cells under various conditions. However, the identification of parameters is challenging owing to their multimodality and nonlinearity. To this end, we propose an improved differential evolution algorithm based on selective perturbation (SPIDE) to solve the parameter identification problem of PV models. The innovations of the article can be summarized as follows: First, a population center-based mutation strategy is proposed to perturb stagnant individuals. Second, a new parameter adaptation technique is proposed, in which the scale factor $F$ is generated based on the wavelet basis function and Cauchy distribution according to different stages of evolution. Third, a perturbation mechanism based on the t-distribution probability density function is incorporated into the crossover operation, aiming to enhance population diversity. Experimental results of both PV models and the universal test-bed demonstrate the superiority of our SPIDE algorithm.