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

Abstract

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