Although solar cell parameters are generally measured at 20-30 C°, flat plate modules normally operate at 40-50 C° under terrestrial conditions and even higher temperatures are used for some concentrator cell applications. Therefore it is interesting to calculate the dependence of cell parameters on temperature. In this paper a simple formulation has been derived for obtaining the temperature dependence of open circuit voltage Voc, short circuit current density Jsc, fill factor FF, and conversation efficiency η, for c-Si and a-Si solar cells.
The thermal dependence of the spectral response (i.e. transmission, reflection and time delay ( τ r ) responses) of uniform polymer optical fiber (POF) Bragg gratings has been investigated. In addition to the temperature dependence, the effects of grating strength (kL g ) and fiber index modulation ( ∆ n) have been investigated. Besides high capability of tunable wavelength due to the unique large and negative thermo-optic coefficient of POF, the spectral response for POF Bragg gratings show high stability and larger spectrum bandwidth with temperature variation compare with the silica optical fiber (SOF) Bragg gratings, especially with the increase of the kL g value. It was found that by increasing kL g , the peak reflectance value increases and the bandwidth of the Bragg reflector become narrower. Also it’s shown by increasing the kL g value, τ r deceasing significantly and reach its minimum value at the designed wavelength ( λ B ). Furthermore, the τ r for POF Bragg gratings is less than that for SOF Bragg gratings at the same value of kL g . Also it’s found that the peak reflectivity value increases to around 60% when the ∆ n value increases from 1 ˣ 10 -4 to 5 ˣ 10 -4 .
Growing interests in nature-inspired computing and bio-inspired optimization techniques have led to powerful tools for solving learning problems and analyzing large datasets. Several methods have been utilized to create superior performance-based optimization algorithms. However, certain applications, like nonlinear real-time, are difficult to explain using accurate mathematical models. Such large-scale combination and highly nonlinear modeling problems are solved by usage of soft computing techniques. So, in this paper, the researchers have tried to incorporate one of the most advanced plant algorithms known as Venus Flytrap Plant algorithm(VFO) along with soft-computing techniques and, to be specific, the ANFIS inverse model-Adaptive Neural Fuzzy Inference System for controlling the real-time temperature of a microwave cavity that heats oil. The MATLAB was integrated successfully with the LabVIEW platform. Wide ranges of input and output variables were experimented with. Problems were encountered due to heating system conditions like reflected power, variations in oil temperature, and oil inlet absorption and cavity temperatures affecting the oil temperature, besides the temperature’s effect on viscosity. The LabVIEW design followed and the results figure in the performance of the VFO- Inverse ANFIS controller.