Study of vanadium doped ZnO films prepared by dc reactive magnetron sputtering at different substrate temperatures

ZnO films doped with vanadium (ZnO:V) have been prepared by dc reactive magnetron sputtering technique at different substrate temperatures (RT–500 C). The effects of the substrate temperature on ZnO:V films properties have been studied. XRD measurements show that only ZnO polycrystalline structure has been obtained, no V2O5 or VO2 crystal phase can be observed. It has been found that the film prepared at low substrate temperature has a preferred orientation along the (002) direction. As the substrate temperature is increased, the (002) peak intensity decreases. When the substrate temperature reaches the 500 C, the film shows a random orientation. SEM measurements show a clear formation of the nano-grains in the sample surface when the substrate temperature is higher than 400 C. The optical properties of the films have been studied by measuring the specular transmittance. The refractive index has been calculated by fitting the transmittance spectra using OJL model combined with harmonic oscillator.

CuxSnSx+1 (x = 2, 3) thin films grown by sulfurization of metallic precursors deposited by dc magnetron sputtering

We report the results of the growth of Cu-Sn-S ternary chalcogenide compounds by sulfurization of dc magnetron sputtered metallic precursors. Tetragonal Cu2SnS3 forms for a maximum sulfurization temperature of 350 ºC. Cubic Cu2SnS3 is obtained at sulfurization temperatures above 400 ºC. These results are supported by XRD analysis and Raman spectroscopy measurements. The latter analysis shows peaks at 336 cm-1, 351 cm-1 for tetragonal Cu2SnS3, and 303 cm-1, 355 cm-1 for cubic Cu2SnS3. Optical analysis shows that this phase change lowers the band gap from 1.35 eV to 0.98 eV. At higher sulfurization temperatures increased loss of Sn is expected in the sulphide form. As a consequence, higher Cu content ternary compounds like Cu3SnS4 grow. In these conditions, XRD and Raman analysis only detected orthorhombic (Pmn21) phase (petrukite). This compound has Raman peaks at 318 cm-1, 348 cm-1 and 295 cm-1. For a sulfurization temperature of 450 ºC the samples present a multi-phase structure mainly composed by cubic Cu2SnS3 and orthorhombic (Pmn21) Cu3SnS4. For higher temperatures, the samples are single phase and constituted by orthorhombic (Pmn21) Cu3SnS4. Transmittance and reflectance measurements were used to estimate a band gap of 1.60 eV. For comparison we also include the results for Cu2ZnSnS4 obtained using similar growth conditions.

Cu2ZnSnS4 solar cells prepared with sulphurized dc-sputtered stacked metallic precursors

In the present work we report the details of the preparation and characterization results of Cu2ZnSnS4 (CZTS) based solar cells. The CZTS absorber was obtained by sulphurization of dc magnetron sputtered Zn/Sn/Cu precursor layers. The morphology, composition and structure of the absorber layer were studied by scanning electron microscopy, energy dispersive spectroscopy, X-ray diffraction and Raman scattering. The majority carrier type was identified via a hot point probe analysis. The hole density, space charge region width and band gap energy were estimated from the external quantum efficiency measurements. A MoS2 layer that formed during the sulphurization process was also identified and analyzed in this work. The solar cells had the following structure: soda lime glass/Mo/CZTS/CdS/i-ZnO/ZnO:Al/Al grid. The best solar cell showed an opencircuit voltage of 345 mV, a short-circuit current density of 4.42 mA/cm2, a fill factor of 44.29% and an efficiency of 0.68% under illumination in simulated standard test conditions: AM 1.5 and 100 mW/cm2.

DC Fuzzy Multicriteria Approach to Increase the Probability of Delivering Power in Distribution Networks

This paper proposes a methodology to increase the probability of delivering power to any load point through the identification of new investments. The methodology uses a fuzzy set approach to model the uncertainty of outage parameters, load and generation. A DC fuzzy multicriteria optimization model considering the Pareto front and based on mixed integer non-linear optimization programming is developed in order to identify the adequate investments in distribution networks components which allow increasing the probability of delivering power to all customers in the distribution network at the minimum possible cost for the system operator, while minimizing the non supplied energy cost. To illustrate the application of the proposed methodology, the paper includes a case study which considers an 33 bus distribution network.

Increase of the Delivered Power Probability in Distribution Networks using Pareto DC Programming

A methodology to increase the probability of delivering power to any load point through the identification of new investments in distribution network components is proposed in this paper. The method minimizes the investment cost as well as the cost of energy not supplied in the network. A DC optimization model based on mixed integer non-linear programming is developed considering the Pareto front technique in order to identify the adequate investments in distribution networks components which allow increasing the probability of delivering power for any customer in the distribution system at the minimum possible cost for the system operator, while minimizing the energy not supplied cost. Thus, a multi-objective problem is formulated. To illustrate the application of the proposed methodology, the paper includes a case study which considers a 180 bus distribution network

Modelação e identificação de motor DC

O modelo matemático de um sistema real permite o conhecimento do seu comportamento dinâmico e é geralmente utilizado em problemas de engenharia. Por vezes os parâmetros utilizados pelo modelo são desconhecidos ou imprecisos. O envelhecimento e o desgaste do material são fatores a ter em conta pois podem causar alterações no comportamento do sistema real, podendo ser necessário efetuar uma nova estimação dos seus parâmetros. Para resolver este problema é utilizado o software desenvolvido pela empresa MathWorks, nomeadamente, o Matlab e o Simulink, em conjunto com a plataforma Arduíno cujo Hardware é open-source. A partir de dados obtidos do sistema real será aplicado um Ajuste de curvas (Curve Fitting) pelo Método dos Mínimos Quadrados de forma a aproximar o modelo simulado ao modelo do sistema real. O sistema desenvolvido permite a obtenção de novos valores dos parâmetros, de uma forma simples e eficaz, com vista a uma melhor aproximação do sistema real em estudo. A solução encontrada é validada com recurso a diferentes sinais de entrada aplicados ao sistema e os seus resultados comparados com os resultados do novo modelo obtido. O desempenho da solução encontrada é avaliado através do método das somas quadráticas dos erros entre resultados obtidos através de simulação e resultados obtidos experimentalmente do sistema real.