SCSO: snake optimization with sine-cosine algorithm for parameter extraction of solar photovoltaic models

Solar power generation is a clean and sustainable energy source. To ensure the efficient operation of photovoltaic (PV) systems, it is essential to develop accurate equivalent models of PV cells and precisely determine their unknown parameters. However, due to the nonlinear and multimodal characteristics of PV systems, accurately extracting PV parameters remains a significant challenge. This paper proposes a hybrid Snake Optimization combined with a Sine–Cosine Algorithm (SCSO) to address the PV parameter extraction problem. The proposed algorithm incorporates three key improvements: (1) integration of the Sine–Cosine Algorithm to enhance the bio-inspired Snake Optimization, balancing exploration and exploitation; (2)The parameters C1 and C2 are adaptively adjusted, and the Newton–Raphson method is introduced to accelerate the algorithm’s convergence speed which accelerates convergence; and (3) application of a lens imaging reverse learning strategy to improve exploration capabilities and population diversity, preventing the algorithm from becoming trapped in local optima. First, the performance of the SCSO algorithm is qualitatively analyzed using the CEC2022 test functions. Then, the algorithm is applied to extract parameters for three different PV modules. Finally, two commercial models (TFST 40 and MCSM 55) are tested under varying environmental conditions to validate the algorithm’s accuracy. Experimental results demonstrate that SCSO outperforms several state-of-the-art metaheuristic algorithms, achieving higher precision and faster convergence.