One of the options to ensure the requirements in education modernization is the use of virtual reality tools, such as simulators, adding factors immersion and interactivity in the student learning process. Combine these benefits within the context of wind energy education, which has been disseminated every year in Brazil and mainly in the state of Rio Grande do Norte, makes the consolidation of this highly relevant technology. Therefore, this work deals with the construction of a 3D simulation tool on free platforms that will bring in its structure the key concepts inherent in wind energy portrayed in an interactive and immersive way to the user.

O modelo de educação nas últimas décadas passou por mudanças consideráveis do ponto de vista didático. Com o avanço da tecnologia, a velocidade de obtenção de informações aumenta e o uso de ferramentas que permitem a visualização dinâmica de conteúdos torna-se um forte aliado ao processo de ensino-aprendizagem. Este trabalho trata da criação de uma ferramenta interativa para visualizar a propagação de ondas de tensão em uma linha de transmissão similar ao que acontece à transmissão e reflexão de ondas eletromagnéticas que incidem em meios dielétricos diferentes, permitindo a inserção de dados pelo usuário, a fim de facilitar a consolidação do conhecimento. O modelo foi criado a partir do GUIDE do MATLAB, que possui uma interface gráfica com botões de ação que ao serem clicados executam ações como calcular e exibir o gráfico animado. É importante ressaltar que a criação do programa, além de facilitar o entendimento, também permite maior familiarização com o software de simulação computacional e com os parâmetros utilizados na análise transitória de linhas de transmissão. Assim, a análise dos transitórios de ondas de tensão em linhas de transmissão, que consiste em uma difícil área de aprendizagem, devido ao seu caráter teórico, torna-se de fácil assimilação com o uso dessa ferramenta didático-computacional.

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This paper presents the implementation of a fuzzy control strategy for speed regulation of an electromagnetic frequency regulator (EFR) prototype, aiming to eliminate the dependence on knowledge of physical parameters in the most diverse operating conditions. Speed multiplication is one of the most important steps in wind power generation. Gearboxes are generally used for this purpose. However, they have a reduced lifespan and a high failure rate, and are still noise sources. The search for new ways to match the speed (and torque) between the turbine and the generator is an important research area to increase the energy, financial, and environmental efficiency of wind systems. The EFR device is an example of an alternative technology that this team of researchers has proposed. It considers the main advantages of an induction machine with the rotor in a squirrel cage positively. In the first studies, the EFR control strategy consisted of the conventional PID controllers, which have several limitations that are widely discussed in the literature. This strategy also limits the EFR’s performance, considering its entire operating range. The simulation program was developed using the Matlab/Simulink platform, while the experimental results were obtained in the laboratory emulating the EFR-based system. The EFR prototype has 2 poles, a nominal power of 2.2 kW, and a nominal frequency of 60 Hz. Experimental results were presented to validate the efficiency of the proposed control strategy.

A device known as a pipeline inspection gauge (PIG) runs through oil and gas pipelines which performs various maintenance operations in the oil and gas industry. The PIG velocity, which plays a role in the efficiency of these operations, is usually determined indirectly from odometers installed in it. Although this is a relatively simple technique, the loss of contact between the odometer wheel and the pipeline results in measurement errors. To help reduce these errors, this investigation employed neural networks to estimate the speed of a prototype PIG, using the pressure difference that acts on the device inside the pipeline and its acceleration instead of using odometers. Static networks (e.g., multilayer perceptron) and recurrent networks (e.g., long short-term memory) were built, and in addition, a prototype PIG was developed with an embedded system based on Raspberry Pi 3 to collect speed, acceleration and pressure data for the model training. The implementation of the supervised neural networks used the Python library TensorFlow package. To train and evaluate the models, we used the PIG testing pipeline facilities available at the Petroleum Evaluation and Measurement Laboratory of the Federal University of Rio Grande do Norte (LAMP/UFRN). The results showed that the models were able to learn the relationship among the differential pressure, acceleration and speed of the PIG. The proposed approach can complement odometer-based systems, increasing the reliability of speed measurements.