Parameters Identification of Photovoltaic Cells Based on Differential Evolution Algorithm

Abstract

For the complex nonlinear model of photovoltaic cells, traditional evolution strategy is easy to fall into the local optimal and its identification time is too long when taking parameters identification, then the difference algorithm is proposed in this study, which is to solve the problems of parameter identification in photovoltaic cell model, where it is very difficult to achieve with other identification algorithms. In this method, the random data is selected as the initial generation; the successful evolution to the next generation is done through a certain strategy of difference algorithm, which can achieve the effective identification of control parameters. It is proved that the method has a good global optimization and the fast convergence ability, and the simulation results are shown that the differential evolution has high identification ability and it is an effective method to identify the parameters of photovoltaic cells, where the photovoltaic cells can be widely used in other places with these parameters.