Key points are not available for this paper at this time.
Accurate parameter identification of photovoltaic (PV) models is essential for the optimal operation and control of PV systems. However, PV cell modeling exhibits nonlinearity and involves numerous challenging-to-solve unknown parameters, thereby reducing the utilization efficiency of solar energy in PV systems. Therefore, this paper proposes an enhanced Snake algorithm (ISASO) that integrates Subtraction Average-Based Optimization (SABO) to address the shortcomings of traditional PV model parameter identification methods, such as low accuracy, slow convergence, and susceptibility to local optima. The SABO algorithm, which updates the positions of search agents using a consistent arithmetic mean position throughout the optimization process, demonstrates high convergence. By integrating SABO's global search strategy into the exploration phase of SO, the global search capability of SO is further enhanced, mitigating the risk of early local optima in the original SO. Additionally, the Tent chaotic map initialization method is incorporated into standard SO to improve the quality of the initial population and enhance population diversity. A dynamic learning factor and adaptive inertia weight strategy are also employed to accelerate the convergence speed of the SO algorithm, balancing its exploration and exploitation capabilities. To validate the performance of ISASO, it is applied to the CEC2005 benchmark functions and employed to identify the optimal parameters of various PV models. Statistical and analytical results reveal that ISASO markedly outperforms existing methods in parameter identification accuracy and reliability, achieving the lowest Root Mean Square Error (RMSE) values between standard and simulated data. Additionally, the superior performance of ISASO is further verified by comparative analysis with existing meta-heuristic algorithms and the Friedman mean ranking statistical method. Therefore, ISASO can be considered as a reliable and effective method to accurately estimate solar PV model parameters.
Mai et al. (Sat,) studied this question.