Navegando por Assunto "KDD (Knowledge discovery in databases)"

Agora exibindo 1 - 2 de 2

Acesso aberto (Open Access)
Análise dos impactos harmônicos na qualidade da energia elétrica utilizando kdd – estudo de caso na Universidade Federal do Pará
(Universidade Federal do Pará, 2019-03-18) SILVA, Waterloo Ferreira da; TOSTES, Maria Emília de Lima; http://lattes.cnpq.br/4197618044519148
The present work presents an analysis of data related to Power Quality (PQ), the increasing use of nonlinear loads, equipment based on power electronics in residential, commercial and industrial installations are contributing to the significant increase in the levels of harmonic distortion of current and, consequently, of voltage, as observed in the Brazilian electricity distribution system. It was developed in Brazil, the distribution procedures in the national electricity system (PRODIST), created and developed by the National Electric Energy Agency (ANEEL). PRODIST aims to standardize and standardize activities related to energy distribution, including product quality standards. In order to monitor the quality of the product "electric energy" through the harmonic content generated by the electric network of the institution, a methodology is proposed for the analysis using computational intelligence (CI) and data mining techniques to analyze the data collected by meters of energy quality installed in the main sectors of this institution and at the point of common coupling of the consumer and consequently establish the relationship between the harmonic currents of the nonlinear loads with the harmonic distortion at the common coupling point. The KDD process was applied, including the collection, selection, cleaning, integration, transformation and reduction, mining, interpretation and evaluation of the data, in order to monitor the quality of the product "electric energy" through the harmonic content generated by the electric grid. educational institution. In the "Data Mining" data mining phase, the Naive Bayes classifier was used. The obtained results showed that the KDD process has applicability in the analysis of the Total Harmonic Distortion of Voltage at the Common Coupling Point and can be applied in any commercial, residential and industrial area.
Acesso aberto (Open Access)
Identificação e estimação de ruído em redes DSL: uma abordagem baseada em inteligência computacional
(Universidade Federal do Pará, 2012-01-25) FARIAS, Fabrício de Souza; COSTA, João Crisóstomo Weyl Albuquerque; http://lattes.cnpq.br/9622051867672434
This paper proposes the use of computational intelligence techniques aiming to identify and estimate the noise power in Digital Subscriber Line (DSL) networks on real time. A methodology based on Knowledge Discovery in Databases (KDD) for detect and estimate noise in real time, was used. KDD is applied to select, pre-process and transform data before data mining step. For noise identification the traditional backpropagation algorithm based on Artificial Neural Networks (ANN) is applied aiming to identify the predominant noise during the collection of information from the user's modem and the DSL Access Multiplexer (DSLAM). While the algorithm for noise estimation, linear regression and a hybrid algorithm consisting of Fuzzy with linear regression are applied to estimate the noise power in Watts. Results show that the use of computational intelligence algorithms such as RNA are promising for noise identification in DSL networks, and algorithms such as linear regression and fuzzy with linear regression (FRL) are promising for noise estimation in DSL networks.