Parameter Identification of Photovoltaic Models Based on Improved Adaptive Differential Evolution Algorithm

To enhance the parameter identification accuracy of photovoltaic (PV) models, this paper proposes an improved adaptive differential evolution algorithm. Building upon the Adaptive Differential Evolution (JADE), the proposed algorithm incorporates a mutation strategy based on successful evolutionary experiences and a crossover rate sorting mechanism, thereby enhancing the utilization of local historical information and the population's elite individual information during the evolutionary process. To validate the algorithm's performance, it is applied to two PV models and compared with four state-of-the-art algorithms. Experimental results demonstrate that the proposed algorithm achieves optimal results in PV model parameter identification and exhibits faster convergence speed.