Mayfly optimization algorithm combined with opposition-based learning for extracting parameters of photovoltaic cells

Simulating the electrical behavior of photovoltaic (PV) systems through a mathematical model is crucial for optimal design, performance evaluation, and control of PV systems. However, building an accurate model for PV systems is still difficult because the parameters of PV cells are unknown in advance or vary in practical operations. To address this issue, a new method based on an improved mayfly optimization algorithm (MOA) is proposed to estimate the unknown parameters of PV cells. To enhance the exploration ability, the opposition-based learning (OBL) strategy is integrated into the position update of mayflies. In addition, to make a better balance between exploration and exploitation, an adaptive gravity coefficient is adopted. The improved MOA is called the OBL-MOA and applied to identify the parameters of the single diode model, double diode model, and PV modules. Extensive experiments have been conducted, and results show that the proposed OBL-MOA is superior to most meta-heuristic algorithms and can achieve more accurate and reliable parameter extraction of PV cells.