Hybrid Algorithm for Optimizing Parameter in the Double Diode Model

The non-linear current versus voltage (I-V) properties of solar photovoltaic (PV) make modeling challenging. Optimization algorithms are the most effective methods for determining the parameters of nonlinear equations. While numerous optimization strategies are utilized to estimate solar PV parameters, the best optimized results have yet to be obtained. In this publication, a novel suggested hybrid method, Grey Wolf Optimization and Cuckoo Search method (GWOCSA), is created for identifying the solar PV parameter estimate of a double diode (DD) model. The precision and convergence time of the devised framework for the double diode model of solar PV are compared to the results produced by Grey Wolf Optimization (GWO), Cuckoo Search Algorithm (CSA), Particle Swarm Optimization (PSO), Multi Verse Optimization (MVO), and Sine Cosine Algorithm (SCA). The results clearly show that the proposed hybrid algorithm can anticipate accurate optimal values with fewer iterations under a variety of environmental situations. A variety of errors were calculated after a detailed investigation of the parameter estimate of the double diode solar PV model. In addition, a non-parametric test was performed to evaluate the accuracy of the proposed technique. The results obtained during this method clearly illustrate that the proposed hybrid algorithm outperforms all other algorithms presented in the study. The proposed algorithm is clearly superior in terms of efficiency and accuracy, making it a preferable choice for use in solar power systems.