In this article, an artificial algorithm called hummingbirds optimization method, named AHA algorithm, is settled to extract accurately the parameters of PV modules under outdoor operational conditions. AHA is the main contribution in this work, regarding its efficiency and good performance in terms of standard deviation (StD), root mean square error (RMSE), sum of squared error (SSE), maximum number of iterations (MaxIt), particularly, for extracting parameter from modules operating in real conditions, and under different temperature and irradiance levels. The AHA is applied on a Polycrystalline-solar panel module type 320W-72P at real operating conditions and on a PV array of three polycrystalline PV modules connected in series. PV cells of the same modules are assumed operating under the same conditions where they share the same electric current and voltage values. In this last pattern, eight different scenarios are chosen. In the first five scenarios, the temperatures are (46.97, 44.23, 42.87, 40.59 and 30.60°C), and the irradiance varies, such as (910, 800.57, 614.13, 415.1 and 200.87W/m2), respectively. In the other three testing scenarios, the solar irradiance is equal (803 W/ m2), and the temperature differs, such as $\text{T}= 47.93\,\,^{\circ }\text{C}$ , 53.38 °C and 36.62°C. Lower values of root mean square errors (RMSE) are achieved ( $9.8602\times {10}^{-4}$ and $2.572533\times {10}^{-2}$ for RTC France PV cell and the 320W-72P module respectively) with 6000 iterations. Moreover, all the eight scenarios are lower than $7.245916\times {10}^{-2}$ for the last case study. Moreover, results show that we could recommend the AHA algorithm as an advanced and efficient method for dealing with real time parameter optimization of photovoltaic modules. In fact, a high closeness between the simulated and the experimental curves is achieved, which indicates the perfectness of this optimization method. Finally, the proposed AHA algorithm can be engaged as tools for the best designing of PV systems.

In arid regions, the behavior of solar panels changes significantly compared to the datasheets provided by the manufacturer. Therefore, the objective of this study is to determine the performance of both polycrystalline and monocrystalline solar modules in an arid region characterized by a large potential for solar irradiation and high temperatures. The influence of environmental parameters, such as temperature and dust, on the output power of solar modules with different technologies (monocrystalline and polycrystalline) has been investigated. The Artificial Hummingbirds Algorithm (AHA) has been used to extract parameters for PV modules. As a result, it has been demonstrated that for high solar irradiation, the polycrystalline PV module experiences a smaller decrease in output power than the monocrystalline PV module as the module temperature increases. The percentage drop in output power is approximately 14% for the polycrystalline PV module and nearly 16% for the monocrystalline PV module. However, for low solar irradiation, it is advisable to use monocrystalline modules, as a 21% decrease in power was observed for polycrystalline modules compared to a 9% decrease for monocrystalline modules. Additionally, the monocrystalline PV module was more affected by dust than the polycrystalline PV module under high solar irradiation conditions, while under low incident solar radiation, the polycrystalline PV module was more affected by dust than the monocrystalline PV module. The power drop of the monocrystalline PV module was greater than that of the polycrystalline PV module for high solar radiation (>500 W/m²). Therefore, the advantage of this proposed work is to recommend the use of polycrystalline solar panels in regions characterized by high solar irradiation and high temperatures instead of monocrystalline solar panels, which are more efficient in regions worldwide characterized by low solar irradiation and low temperatures.