Parameter Extraction of Photovoltaic Module Using Smell Agent Optimization Algorithm

This research proposes a novel approach for accurate parameter extraction of photovoltaic (PV) modules using the Smell Agent Optimization (SAO) algorithm. Accurate PV parameter estimation is essential for reliable modeling, performance optimization, and effective integration into renewable energy systems. The proposed SAO-based method is inspired by agent–environment interactions and is designed to improve estimation accuracy while maintaining computational efficiency. A comprehensive review of existing parameter extraction techniques is conducted to identify limitations related to accuracy and convergence behavior. Based on this analysis, the SAO algorithm is implemented to estimate key PV parameters, including short-circuit current, open-circuit voltage, maximum power point current, and maximum power point voltage. The proposed method is validated through simulations using real performance data from a selected PV module. Results demonstrate a substantial reduction in absolute errors, with current and power extraction errors of 0.06 A and 0.03 W, respectively, representing an improvement of approximately 15% compared to conventional optimization methods. Additionally, the robustness of the SAO algorithm is evaluated under varying environmental conditions, confirming its adaptability and consistent performance. The findings indicate that SAO provides a reliable and efficient solution for PV module parameter extraction.