Solar PV Parameter Extraction Using GWO-PSO Algorithm

In this paper, the proposed optimization approach is hybrid between the Grey Wolf Optimizer and the Particle Swarm Optimization, where it is utilized for extraction parameters in single-diode photovoltaic models that play an important role in accurately simulating and analyzing solar PV systems. These model parameters are highly nonlinear, posing a challenge in achieving the accurate estimation. Accurate values play an important role in the optimization of PV system performance and design. Hybrid GWO-PSO does the initial global search to identify promising regions of the solution space by using the exploration ability of GWO and refines those regions by utilizing the convergence strengths of PSO for the optimal values of the parameters. In order to enhance the precision further, the Nelder-Mead method is applied as a local search in fine-tuning the final solutions. Secondly, a penalty mechanism is there which deals with the negative values of R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> scores and tries that the model should be more predictable and not get into solutions at low quality. For added robustness, we're using a Huber loss function in the objective reducing the effect of outliers towards the estimation of parameters involved. The proposed hybrid methodology is tested on real data of PV I-V for results evaluation using error metrics such as RMSE, MAE, and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>. This will also reflect that the accuracy for the estimation of the algorithm is better than if applied GWO-PSO or PSO separately. Better parameter estimations lead to enhanced modeling of PV systems in multiple environmental conditions and push solar PV technology further both theoretically and practically. This, therefore, suggests an efficient, accurate extraction of PV parameters, which promises a better performance monitoring and system optimization within the solar energy industry.