Orthogonal learning-based Gray Wolf Optimizer for identifying the uncertain parameters of various photovoltaic models

Defining the optimal parameters for photovoltaic (PV) system model is essential design, evolution, development, estimation, and analysis of PV power system. Therefore, it crucial to properly identify best models based on modern computational techniques. As a result, this research proposes new Orthogonal-Learning-Based Gray Wolf Optimizer (OLBGWO) identifying uncertain in cell using local exploratory approach. The orthogonal-learning-based (OLB) technique enhances exploitation exploration capabilities original (GWO) modified vector parameter called , which promotes highly reliable balance between phases algorithm. During iterative procedure OLBGWO, OLB methodology employed obtain solution weaker populations guides population examine prospective search area. Additionally, an exponential decay function used reduce value . proposed approach solve system's estimation problem. presented OLBGWO algorithm estimates single-diode (SDM), double-diode (DDM), module model. OLBGWO's performance compared those other competing algorithms demonstrate its superiority. simulation results that provides fast convergence speed while maintaining high accuracy.