Electrical parameters identification for three diode photovoltaic based on the manta ray foraging optimization with dynamic fitness distance balance

Abstract

This study focuses on the critical task of accurately simulating and operating solar cells and photovoltaic modules by acquiring essential model parameters through experimental data. These parameters play a pivotal role in assessing system performance under varying environmental conditions. However, due to the inherent nonlinearity of solar photovoltaic systems, a dependable algorithm is imperative for precise modeling. Introducing the MRFO-dFDB algorithm (Manta ray foraging optimization with Dynamic fitness distance balance), designed to address the intricate challenges associated with modeling solar photovoltaic systems. This algorithm harnesses fitness distance balance to maintain a harmonious balance between exploration and exploitation within the search space. Its adaptability to shifts in the search landscape, achieved through dynamic adjustments to distance and fitness metrics, enhances its versatility and effectiveness. Our extensive evaluation encompasses a comparative analysis of the MRFO-dFDB algorithm against six other optimization methods across diverse solar module types, including STP6–120/36, Photowatt-PWP201, XKD_50W, and XHYG-10W. The outcomes underscore the MRFO-dFDB algorithm's remarkable equilibrium between precision and computational efficiency. Numerical findings provide concrete evidence of the MRFO-dFDB algorithm's capabilities. For example, it achieves an RMSE of 0.002087 A and an NRMSE of 0.0015639 for the Photowatt-PWP201 module, underscoring its proficiency in parameter extraction. Similarly, for the STP6–120/36 module, it records an RMSE of 0.015194 A, highlighting its effectiveness in modeling.