Enhancing Parameters Estimation of Renewable Energy Systems Using YAK Algorithm

In this study, we introduce the Yak-inspired Adaptive Kinetic (YAK) optimization method as an effective approach for estimating critical parameters in two key renewable energy systems: solar cells and wind turbines. The YAK method is applied to both single-diode and double-diode solar cell models to estimate essential physical parameters, such as photocurrent, reverse saturation current, ideality factor, series resistance, and parallel resistance. Additionally, the method is employed to estimate wind turbine parameters, including rotor diameter, power coefficient (Cp), and air density (ρ). We compare the performance of the YAK method against several widely used optimization techniques, including Particle Swarm Optimization (PSO), Grasshopper Optimization Algorithm (GOA), Harris Hawks Optimization (HHO), Social Spider Algorithm (SSA), Improved Artificial Bee Optimization (IABO), Rat Swarm Optimizer (RSO), and Ant Lion Optimizer (ALO). The performance of each method is evaluated based on error metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), as well as the computational time required to achieve optimal results. While the YAK method shows promise as a viable option, the results indicate that other algorithms, particularly PSO, achieved better metric values in several cases. Specifically, the YAK method demonstrates improvements in computational speed compared to some alternatives, suggesting its potential effectiveness in certain scenarios. This comparison highlights the importance of exploring various optimization methods for parameter estimation in renewable energy systems.