Accurate control of the operating temperature is essential for achieving optimal efficiency in the use of solar panels. The Normal Operating Cell Temperature (NOCT) is a widely used method for estimating module temperature, but it is not always accurate for all conditions. In this paper, the authors propose four different models for estimating module temperature using various techniques including Neural Networks, a linear method proposed by Ross, and a Fitting method. The objective is to find more accurate methods than the NOCT model. The results show that all four models provide good agreement between measured and calculated module temperatures, with R² > 0.91 and RMSE < 3.74 for clear days, and considering the resources of time, cost, and expertise necessary for simulating or evaluating the proposed model, the two models based on RN neural networks offers a more cost-effective solution, by utilizing straightforward mathematical skills, the RN model demonstrates applicability in any environment, making it a preferable choice over the NOCT model for predicting polycrystalline photovoltaic module temperature under different climatic conditions. The proposed models can be used to estimate PV module temperature with good precision under different climatic conditions.
The aim of this study was to evaluate a performance analysis of a 50 MWp solar plant connected to the medium voltage electrical grid installed in the Saharan environment of Nouakchott, Mauritania. This study is done in two seasons characterizing the climate of Nouakchott, then in three typical days based on the measurement data that are obtained from the site of the installation. The measurements were collected daily, to improve the performance evaluation, real-time measurements with a step of 10 seconds for solar irradiation, ambient temperature, module temperature, wind speed, and electrical parameters. The performance evaluation was done based on the IEC 61724 standard to study the comportment of the power plant during different weather conditions. The total energy produced by the plant in May 2020 dry season is 10.559 GWh. However, the total energy produced in November 2019 wet season was 8.132 GWh. Besides, the energy injected by the plant into the grid was for a clear day 263.87 MWh, while for a cloudy day was 118.41 MWh, while it was 39.81 MWh for a sandstorm day. The results showed that temperature and irradiation play an important role in the performance of the system.
This paper deals with a solar cooker that functions with thermosiphon and uses vegetable oil as coolant.The use of this fluid requires the knowledge of its density, its dynamic viscosity (variable according to its temperature) and of its heat-storage capacity.Therefore, we conducted an experimental determination of these properties for the cotton seed oil, the groundnut oil, the oil of CANARIUM, the artisanal red oil, SODEPALM red oil, the industrial palm oil, the oil of Shea tree, the Coconut oil and the oil of "KIBI".The results are presented in graphs as regards the density and dynamic viscosity, and in a table with regard to the heat-storage capacity.The knowledge of these various sizes enables to make a good choice of the coolant that best adapts to this type of equipment.
