Given the scenarios of increasing world population, the concentration of CO2, fuel costs, global consumption of energy and climate change, there was a need to search for alternative energy sources. In this context, solar photovoltaic, the result of research and investments over the past five decades, had a great impact in the last decade, recording a significant increase in the production of photovoltaic cells and modules and installations of photovoltaic systems worldwide. One of the goals of this work was to study the behavior of PV modules by partial shading situations in different conditions of temperature and solar radiation. Within this partial shading scenario that impacts virtually all electrical parameters of a module, the study and calculation of the series resistance of the modules was done in conditions of partial shading proposals to verify the relationship between the value of the series resistance and the state of shading partial, allowing identification of a possible state of shading from monitoring the resistance series. Another objective of this work was to apply Artificial Intelligence (AI) resources in the form of Artificial Neural Networks (ANN) to, after the proper training and learning of ANN from the database collected in the field under conditions of partial shade, they can identify the parameters of the PV modules within the various conditions of partial shading proposals. To pursue the objectives of this work four photovoltaic modules were used, two with 40 W nominal power with over fifteen years of manufacturing, assigned by the Instituto Federal de Goiás (IFGoiano) Urutaí Campus, at the city of Urutaí-GO and two new without any use of 75 W nominal power assigned by the Center for Research on Alternative Energy Sources, School of Electrical Engineering, Universidade Federal de Uberlândia (UFU). / Diante dos cenários de aumento da população mundial, da concentração de CO2 ,dos custos dos combustíveis, do consumo mundial de energia e das alterações climáticas, surgiu a necessidade de se buscar por fontes de energias alternativas. Neste contexto, a energia solar fotovoltaica, fruto de investigações e investimentos realizados nas últimas cinco décadas, teve um grande impacto na última década, registrando um aumento significativo na produção de células e módulos fotovoltaicos e instalações de sistemas fotovoltaicos no mundo todo. Um dos objetivos deste trabalho foi estudar o comportamento de módulos fotovoltaicos mediante situações de sombreamento parcial em diferentes condições de temperatura e radiação solar. Dentro deste cenário de sombreamento parcial que causa impacto praticamente em todos os parâmetros elétricos de um módulo, foi feito o estudo e cálculo da resistência série dos módulos nas condições de sombreamento parcial propostas para verificar a relação entre o valor da resistência série e o estado de sombreamento parcial, possibilitando uma possível identificação do estado de sombreamento a partir do monitoramento da resistência série. Outro objetivo deste trabalho foi aplicar recursos de Inteligência Artificial (IA) na modalidade de Redes Neurais Artificiais (RNAs) para, após o devido treinamento e aprendizado das RNAs a partir do banco de dados colhidos em campo sob condições de sombreamento parcial, elas poderem identificar os parâmetros do módulo fotovoltaico dentro das diversas condições de sombreamento parcial propostas. Para a busca dos objetivos deste trabalho foram utilizados quatro módulos fotovoltaicos sendo dois de 40 W de potência nominal com mais de quinze anos de fabricação, cedidos pelo Instituto Federal Goiano (IFGoiano) Campus Urutaí, da cidade de Urutaí-GO e dois novos e sem uso, de 75 W de potência nominal cedidos pelo Núcleo de Pesquisa em Fontes Alternativas de Energia da Faculdade de Engenharia Elétrica da Universidade Federal de Uberlândia (UFU). / Mestre em Ciências

A study of defects in semiconductors is vital as defects tend to influence device operation by modifying their electrical and optoelectronic properties. This influence can at times be desirable in the case of fast switching devices and sometimes undesirable as they may reduce the efficiency of optoelectronic devices. ZnO is a wide bandgap material with a potential for fabricating UV light emitting diodes, lasers and white lighting devices only after the realization of reproducible p-type material. The realization of p-type material is greatly affected by doping asymmetry. The self-compensation behaviour by its native defects has hindered the success in obtaining the p-type material. Hence there is need to understand the electronic properties, formation and annealing-out of these defects for controlled material doping. Space charge spectroscopic techniques are powerful tools for studying the electronic properties of electrically active defects in semiconductors since they can reveal information about the defect “signatures”. In this study, novel Schottky contacts with low leakage currents of the order of 10-11 A at 2.0 V, barrier heights of 0.60 – 0.80 eV and low series resistance, fabricated on hydrogen peroxide treated melt-grown single crystal ZnO samples, were demonstrated. Investigations on the dependence of the Schottky contact parameters on fabrication techniques and different metals were performed. Resistive evaporation proved to produce contacts with lower series resistance, higher barrier heights and low reverse currents compared to the electron-beam deposition technique. Deep level transient spectroscopy (DLTS) and Laplace-DLTS have been employed to study the electronic properties of electrically active deep level defects in ZnO. Results revealed the presence of three prominent deep level defects (E1, E2 and E3) in the as-received ZnO samples. Electron-beam deposited contacts indicated the presence of the E1, E2 and E3 and the introduction of new deep level defects. These induced deep levels have been attributed to stray electrons and ionized particles, present in the deposition system during contact fabrication. Exposure of ZnO to high temperatures induces deep level defects. Annealing samples in the 300°C – 600°C temperature range in Ar + O2 induces the E4 deep level with a very high capture cross-section. This deep level transforms at every annealing temperature. Its instability at room temperature has been demonstrated by a change in the peak temperature position with time. This deep level was broad, indicating that it consists of two or more closely spaced energy levels. Laplace-DLTS was successfully employed to resolve the closely spaced energy levels. Annealing samples at 700°C in Ar and O2 anneals-out E4 and induces the Ex deep level defect with an activation enthalpy of approximately 160 – 180 meV. Vacuum annealing performed in the 400°C – 700°C temperature range did not induce any deep level defects. Since the radiation hardness of ZnO is crucial in space applications, 1.6 MeV proton irradiation was performed. DLTS revealed the introduction of the E4 deep level with an activation enthalpy of approximately 530 meV, which proved to be stable at room temperature and atmospheric pressure since its properties didn’t change over a period of 12 months. / Thesis (PhD)--University of Pretoria, 2013. / Physics / unrestricted