Parameter extraction of the photovoltaic model via an improved composite differential evolution

To provide more general and efficient photovoltaic (PV) systems, performing the simulation, control, and optimization on the corresponding PV model are essential and significant. The accurate degree of the related parameters is crucial for the performance of PV models. However, when identifying the parameters by an evolutionary algorithm, complex characteristics of the PV system propose difficulty for the solver. For this purpose, this paper develops an improved composite differential evolution (ICoDE) algorithm. In ICoDE, each individual generates three offspring by using three strategies from the strategy pool. During the evolution, the selection probabilities of different search strategies are adjusted dynamically to improve diversity in the early stage and speed convergence in the late stage. The proposed ICoDE is tested on the single diode model, double diode mode, and PV module. Compared with seven advanced optimization algorithms, the simulation results indicate the superiority of ICoDE.