Summary
Much of the cost of manufacturing transformers is related to the complexity in the development of its project, which involves many variables, such as different types of materials, methods, and manufacturing processes. Also, it is notoriously difficult to establish standards that relate the transformer characteristics with these design variables, since each, almost always, variable is calculated empirically. One of the most important design variables is the internal temperature of the transformers, which directly influences the lifetime of such equipments. So, a computational tool is under development whose purpose is to automate the design of cast resin dry‐type transformers and minimize the time taken for its completion, by means of artificial neural networks. In this article, we present some results of this tool, which relate some geometrical parameters of the specific transformer with the temperature of its windings. For this, the winding losses and total losses are also estimated. The training of the neural network was done with the test data of about 300 dry‐type transformers from the same manufacturer, so, with the same constructive features. Nevertheless, our results show that the technique is promising because the neural networks were well fitted and its results present errors lower than 1% when compared with data from tests with real transformers. Evidently, the proposed methodology is dependent on the constructive technique; that is, once trained, the neural network can be used only in the design of dry‐type transformers of the same constructive technique as those whose results of the tests were used to train the network. Nevertheless, even if only used to provide the initial parameters for a more complex design tool, the proposed method is useful since it is rapid and has low computational cost.
ResumoEste trabalho tem como proposta a apresentação de um algoritmo descentralizado em tempo fixo (ATEFI), com a finalidade de fornecer planos de tempos semafóricos otimizados. Os resultados do algoritmo serão comparados com o software comercial TRANSYT. O objetivo é a minimização do atraso e a comparação de estratégias que buscam a solução para os problemas de gerenciamento de tráfego urbano a baixo custo e adaptados à realidade da malha viária nacional. Palavras-chave: Engenharia de tráfego, Otimização, Controle semafórico.
AbstractThis work proposes the presentation of a decentralized fixed time algorithm (ATEFI), with the purpose of finding optimized traffic light times. The results of the algorithm will be compared with commercial software called TRANSYT. The objective is to minimize the delay and the comparison of different strategies, trying to obtain a low cost solution for the urban traffic management problems.
Certain companies in brazilian's electricity distribuction sector have deficiencies in relation to the power quality, highlighted by the high values of their distribution indicators and, consequently, financial compensation paid to consumers for violating the so-called continuity goals. Given this motivation, this paper proposes a multiple linear regression model to assess the impact of distribution indicators on the ANEEL Consumer Satisfaction Index. For the data structure of the model, panel data was used for 26 utilities during the period 2010 to 2019. The results obtained with the panel data model indicate that, of the variables tested, only one does not have statiscally significance with the model, obtaining parameters that can indicate among the distribution indicators adopted, which have the greatest impact on consumer satisfaction. Resumo: Determinadas empresas do setor elétrico brasileiro de distribuição apresentam deficiências em relação à qualidade de fornecimento de energia elétrica, salientadas pelos altos valores de seus indicadores da distribuição e, consequentemente, compensações financeiras pagas aos consumidores pela transgressão das chamadas metas de continuidade. Dada essa motivação, o presente trabalho propõe um modelo de regressão linear múltipla para avaliar o impacto dos indicadores da distribuição no Índice ANEEL de Satisfação do Consumidor. Para a estrutura de dados do modelo, foram utilizados 26 concessionárias durante o período de 2010 a 2019. Os resultados obtidos com o modelo de dados em painel apontam que, das variáveis testadas, apenas uma não possui significância estatística com o modelo, obtendo parâmetros que possam indicar, entre os indicadores da distribuição adotados, quais são os de maior impacto na satisfação do consumidor.
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