Adaptive teaching–learning-based optimization with experience learning to identify photovoltaic cell parameters

Abstract

Parameter identification of photovoltaic cell and module plays a key role in the simulation, evaluation, control, and optimization of photovoltaic systems. In order to improve the accuracy and reliability of the identified parameters, in this paper, a new adaptive teaching–learning-based optimization with experience learning referred as ELATLBO, is proposed. In ELATLBO, the population is first sorted according to the objective function value, and then it is divided into two parts: fit solutions with good objective function values and inferior solutions with poor objective function values. The fit solution that selects the teacher phase with experience learning is used for local search to improve the exploitation capability of the algorithm. While the inferior solution that selects the learner phase with experience learning is applied for global search to enhance the exploration capability of the algorithm. The performance of ELATLBO is verified by testing the photovoltaic cells and module parameter identification problems, i.e., the single diode model, the double diode model, and the single diode photovoltaic module. The simulated result shows that the proposed ELATLBO exhibits remarkable performance on the accuracy and reliability when compared with other reported parameter identification techniques, especially for the double diode model.