Research on parameter identification of photovoltaic model based on Improved Crested Porcupine Optimizer

Precision modeling of photovoltaic systems is essential for technological progress in solar energy applications. To achieve precise extraction of model parameters and enhance photovoltaic model precision, an Improved Crested Porcupine Optimizer (ICPO) incorporating chaotic initialization and Lévy flight perturbations is proposed and applied to PV model parameter extraction. This algorithm solves optimization problems by simulating porcupines’ defense behaviors. It introduces chaotic mapping initialization to avoid initial solution differences affecting optimization and identifies underperforming agents with Levy flight perturbations to evade local optima, significantly accelerating convergence and enhancing global optimization ability. Finally, comparative experiments were conducted with various existing methods under different models and data, fully verifying the superiority of the proposed method.