Abstract: At present, there are some problems in parameter identification of photovoltaic cells, such as low precision, slow speed and poor stability, which need to be improved. To solve these problems, a method of parameter identification of photovoltaic cells based on dung beetle optimization algorithm is proposed in this paper. By introducing Tent chaotic mapping to initialize the population, the initial solution is distributed as evenly as possible in the solution space. Levy flight strategy is added to update individual position of dung beetle during ball rolling, jump out of the local optimal solution, and expand the search scope. The adaptive T-distribution and dynamic selection strategies are adopted, and the T-distribution mutation operator with the number of iterations as the degree of freedom parameter is used to perturbate the dung beetle position, which enhances the global development ability and local exploration ability of the algorithm, and accelerates the convergence speed. The experimental results show that the root-mean square errors of RTC France’s single diode model, double diode model and Photowatt-PWP 201 model are respectively 0.000 986, 0.000 983 and 0.002 425. The method proposed in this paper can identify the parameters of photovoltaic cells faster and more accurately, and has a small error and high stability.

Key words: photovoltaic cells, parameter identification, dung beetle optimization algorithm, meta-heuristic algorithm