Conselho Nacional de Desenvolvimento Científico e Tecnológico / Electrical energy generation with photovoltaic cells is being more utilized. Not only on large scale systems, but also in small ones connected to the grid. Parallel operating with the great generators from power companies, in a non-centralized way of operation, supplying low power, installed in houses, commerce establishments, industry, with the goal to minimize the loss in transmission, for being installed at the same consumption place. This work presents a FPGA device controller of a four transistor flyback inverter for maximum power point in photovoltaic systems. Despite this system has low power it contributes to search simple and low cost alternatives for generating of electrical power in a decentralized manner, which does not use battery banks connected parallel to network of energy near to consumers. / Geração de energia elétrica a partir de painéis fotovoltaicos vem sendo cada vez mais utilizada, não somente em sistemas fotovoltaicos de grande porte, como também em pequenos sistemas conectados a rede CA. Interligada paralelamente aos grandes geradores da concessionária de energia de forma descentralizada em sistemas de pequeno porte e baixas potências, instalados em residências, estabelecimentos comerciais, indústria, com o objetivo de minimizar perdas por transmissão por estarem instalados nos próprios locais. Este trabalho apresenta um controle num dispositivo FPGA de um inversor flyback a quatro transistores para máxima potência em sistemas fotovoltaicos. Apesar da baixa potência este contribui para a busca de alternativas simples e de baixo custo para geração de energia elétrica de forma descentralizada, não utilizando bancos de bateria conectados paralelamente a rede de energia próxima aos consumidores. / Mestre em Ciências

Yes / In order to improve the performance of the reverse osmosis (RO) desalination plant powered by photovoltaic (PV), two cooling systems were proposed in this study to cool the PV and preheating the RO feed water as well. In the cooling design (1), the cooling fluid flows in direct contact with the back surface of the PV through channels of half circular cross-sections. While in the design (2), it flows through channels of squar cross-sections fixed on the PV back surface. Two nano-fluids were also tested as cooling fluid: H2O/CuO and H2O/Al2O3, in addition to distilled water for the purpose of comparison. The effect of changing the weight concentration of the nano-fluid (0.05, 0.1, and 0.15%) on the PV performance was also investigated. The results showed that the PV integrated with the cooling design (1) achieves better performance compared to design (2) at all studied cooling fluids. The improvements in the electric efficiency of the PV integrated with design (1) reached 39.5, 34.8 and 27.3 % when CuO and Al2O3 nano-fluids and distilled water were used as cooling fluid, respectively, compared to the uncooled PV. Based on the obtained experimental results, the PV integrated with design (1) was selected to power the RO with H2O/CuO nano-fluid of weight concentration 0.15% and flow rate 0.15 kg/s being used as the coolant. The RO powered by the improved PV was tested at different salinities of brackish water when the preheating technique was implemented. The results showed that the proposed PV-RO desalination system produces 366 l/day when brackish water of salinity 3000 ppm was used.

This thesis discusses the modeling of a micro-grid with photovoltaic (PV)-wind-fuel cell (FC) hybrid energy system and its operations. The system consists of the PV power, wind power, FC power, static var compensator (SVC) and an intelligent power controller. Wind and PV are primary power sources of the system, and an FC-electrolyzer combination is used as a backup and a long-term storage system. A simulation model for the micro-grid control of hybrid energy system has been developed using MATLAB/Simulink. A SVC was used to supply reactive power and regulate the voltage of the hybrid system. To achieve a fast and stable response for the real power control, the intelligent controller consists of a Radial Basis Function Network-Sliding Mode Control (RBFNSM) and a General Regression Neural Network (GRNN) for maximum power point tracking (MPPT). The pitch angle of wind turbine is controlled by RBFNSM, and the PV system uses GRNN, where the output signal is used to control the DC/DC boost converters to achieve the MPPT.