A twofold hunting trip African vultures algorithm for the optimal extraction of photovoltaic generator model parameters

The development of reliable simulators that finely imitate the behavior of PV devices is vitally important for the design and optimization of efficient and stable photovoltaic systems. In this work, an improved variant of the African Vultures Optimization Algorithm named IAVOA is designed to serve as a powerful tool for extracting the unknown parameters of photovoltaic models. The introduced scheme incorporates a twofold strategy in such a way that allows a portion of the search agents to conduct a global search while the remaining portion performs a local search. The embedded mechanism is based on two equations added to the standard version, and by which the exploration and exploitation capabilities of the algorithm have significantly been fostered. To testify the performance of the IAVOA, a comparative study based on the Root Mean Square Error (RMSE), was conducted on six distinct benchmark PV models, and the obtained results were, in most cases, remarkably superior to the ones achieved by its competitors. The algorithm was able to produce values for the ideality factors that have not been previously found by any existing work to the best of our knowledge. In turn, the Double Diode and Triple Diode models' accuracies were notably improved with RMSE scores of 6.9096 x 10(-4) and 7.4011 x 10(-4) respectively for the RTC France cell, and 1.4251 x 10(-2) for the STP6-120/36 module, outperforming the existing techniques. In light of that, it can be reliably presumed that the IAVOA is indeed a promising algorithm for the electrical characterization of PV devices.

Automated summary

The study presents an improved variant of the African Vultures Optimization Algorithm (IAVOA) for extracting the unknown parameters of photovoltaic models. The algorithm outperforms its competitors in terms of performance and accuracy, making it a promising tool for the design and optimization of photovoltaic systems.