A Hybrid-Strategy-Improved Dragonfly Algorithm for the Parameter Identification of an SDM

Abstract

As primary components of solar power applications, photovoltaic cells have promising development prospects. Due to the characteristics of PV cells, the identification of parameters for circuit models has become a research focus. Among the various methods of parameter estimations, metaheuristic algorithms have attracted significant interest. In this paper, a hybrid-strategy-improved dragonfly algorithm (HIDA) is proposed to meet the demand for high parameter-identification accuracy. Tent chaotic mapping generates the initial position of individual dragonflies and aids in increasing the population diversity. Individual dragonflies can adapt their updated positions to various scenarios using the adjacent position decision approach. The whale optimization algorithm fusion strategy incorporates the spiral bubble-net attack mechanism into the dragonfly algorithm to improve the optimization-seeking precision. Moreover, the optimal position perturbation strategy reduces the frequency of the HIDA falling into local optima from the perspective of an optimal solution. The effectiveness of the HIDA was evaluated using function test experiments and engineering application experiments. Seven unimodal and five multimodal benchmark test functions in 50, 120, and 200 dimensions were used for the function test experiments, while five CEC2013 functions and seven CEC2014 functions were also selected for the experiments. In the engineering application experiments, the HIDA was applied to the single-diode model (SDM), engineering model, double-diode model (DDM), triple-diode model (TDM), and STM-40/36 parameter identification, as well as to the solution of seven classical engineering problems. The experimental results all verify the good performance of the HIDA with high stability, a wide application range, and high accuracy.