Dissertações

URI permanente para esta coleçãohttps://rigalileo.itegam.org.br/handle/123456789/174

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2018 - 20192

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Assunto: search.filters.subject.Harmônicos

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Agora exibindo 1 - 2 de 2
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    Análise de Impactos Harmônicos em Rede de Distribuição de Energia Elétrica de Média Tensão Utilizando Técnicas de Inteligência Computacional
    (Instituto de Tecnologia, 2018) Rivanildo Duarte Almeida; Jandecy Cabral Leite
    The growing demand for electrical energy and the increased use of non-linear loads in distribution systems have intensified the generation of harmonic distortions, compromising the quality of the energy supplied. This work presents an analysis of the harmonic impacts on medium-voltage electrical energy distribution networks, using computational intelligence techniques for modeling, simulation and diagnosis. Methods based on artificial neural networks and genetic algorithms were applied to identify and mitigate harmonic distortions, providing an efficient and automated approach for electrical energy quality control. The results demonstrate the feasibility of applying these techniques, evidencing improvements in the identification of distortions and in the definition of strategies to minimize their adverse effects on the electrical system.
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    Análise dos Impactos Harmônicos na Qualidade da Energia Elétrica Utilizando KDD – Estudo de Caso na Universidade Federal do Pará
    (Instituto de Tecnologia, 2019) SILVA, Waterloo Ferreira da; TOSTES, Maria Emilia de Lima
    This work presents an analysis of data related to Power Quality (PQ), focusing on the significant increase in harmonic distortion levels of current and voltage due to the growing use of non-linear loads and power electronics-based equipment. A methodology was developed using computational intelligence (CI) and data mining techniques to analyze data collected by power quality meters installed at the Federal University of Pará (UFPA). The KDD process was applied, including collection, selection, cleaning, integration, transformation, reduction, mining, interpretation, and evaluation of the data. The Naive Bayes classifier was used in the data mining phase, and the results demonstrated the applicability of the KDD process in the analysis of Total Harmonic Distortion of Voltage at the Common Coupling Point, which can be applied in commercial, residential, and industrial areas.