Accurate characterization of solar photovoltaic (PV) cell behavior is fundamental to designing efficient renewable energy systems and forecasting performance under varying conditions. However, extracting electrical model parameters from real-world PV devices remains challenging due to the complexity of nonlinear equations governing solar cell physics and the computational limitations of traditional optimization methods.
Researchers have addressed this problem by developing a hybrid algorithm that merges two established metaheuristic optimization techniques: the Grey Wolf Optimizer (GWO) and Arithmetic Optimization Algorithm (AOA). The resulting algorithm, designated H-GWOAOA, strategically embeds arithmetic operators into GWO's hierarchical leadership structure to enhance the balance between exploration—searching broad solution spaces—and exploitation—refining promising solutions.
The innovation lies in its selective integration strategy, which improves solution quality without adding computational overhead. Testing on 23 mathematical benchmark functions demonstrated superior convergence behavior compared to GWO, AOA, and other established algorithms. When applied to single-diode, double-diode, and triple-diode PV models, the algorithm achieved root mean square errors as low as 5.51×10⁻⁴, representing significant accuracy gains.
For power system professionals, this capability matters because precise PV parameter identification directly impacts system modeling accuracy, enabling better prediction of power output, voltage behavior, and thermal performance across diverse climate conditions and operating points. This translates to more reliable grid integration planning and optimal sizing of solar installations.
The algorithm's demonstrated robustness across biomedical classification tasks suggests broad applicability beyond photovoltaics. Engineers can leverage this tool to develop more accurate digital twins of solar plants, improve maximum power point tracking algorithms, and enhance forecasting models used in grid planning and renewable energy management systems. The computational efficiency preservation makes deployment practical in both offline design phases and real-time monitoring applications.
