Improved social network search algorithm coupled with Lagrange method for extracting the best parameter of photovoltaic modules and array

Optimizing the operation of a photovoltaic system involves a precise model. The current–voltage characteristic equation remains the best way to obtain its optimal parameters from the model. However, the transcendence of this equation remains a significant problem in the process of extracting these parameters. This paper presents a hybrid method comprised of the improved social network search algorithm and the Lagrange method for obtaining the best parameters for modeling a photovoltaic panel. The proposed method improves the basic algorithm by randomly adding a control parameter through Gaussian and Cauchy distributions, which aim to boost the search space agents to converge to the best solution. Then, the Lagrange method is used to solve the objective function's transcendental problem and to generate the best-estimated current. The proposed method has been applied to three different systems, viz., a photovoltaic array consisting of 18 modules (GL100), a photovoltaic panel (Photowatt PWP module), and a photovoltaic cell (RTC France). The difference in the results obtained vis-a-vis the other metaheuristic methods and those recently proposed for solving the transcendental of the I–V equation demonstrated the efficiency of the proposed algorithm. For the Photowatt PWP PV panel considered as the test system in the literature, the best parameters obtained are 1.0331 A for the photocurrent, 1.1863 µA for the saturation current, 45.3535 for the diode ideality factor, 1.5002 Ω for the series resistance, and 683.8995 Ω for the parallel resistance. These parameters gave a root mean square error of 1.5410 × 10 –3 , which converges at the 600th iteration.