Navegando por Autor "FREIRE, Jean Carlos Arouche"

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Acesso aberto (Open Access)
Análise de desempenho de algoritmos para classificação de sequências representando faltas do tipo curto-circuito em linhas de transmissão de energia elétrica
(Universidade Federal do Pará, 2019-12-05) FREIRE, Jean Carlos Arouche; MORAIS, Jefferson Magalhães de; http://lattes.cnpq.br/5219735119295290; CASTRO, Adriana Rosa Garcez; http://lattes.cnpq.br/5273686389382860
Maintaining power quality in electrical power systems depends on addressing the major disturbances that may arise in their generation, transmission and distribution. Within this context, many studies have been developed aiming to detect and classify short circuit faults in electrical systems through the analysis of the electrical signal behavior. Transmission line fault classification systems can be divided into two types: online and post fault classification systems. In the post-missing scenario the signal sequences to be evaluated for classification have variable length (duration). In sequence classification it is possible to use conventional classifiers such as Artificial Neural Networks, Support Vector Machine, K-nearest neighboors and Random forest. In these cases, the classification process usually requires a sequence preprocessing or a front end stage that converts the raw data into sensitive parameters to feed the classifier, which may increase the computational cost of the classification system. An alternative to this problem is the FBSC-FrameBased-Sequence Classification (FBSC) architecture. The problem with FBSC architecture is that it has many degrees of freedom in designing the model (front end plus classifier) and it should be evaluated using a complete dataset and rigorous methodology to avoid biased conclusions. Considering the importance of using efficient short-circuit fault classification methodologies and mainly with low computational cost, this paper presents the results of the KNN-DTW (K-Nearest Neighbor) algorithm analysis study associated with Dynamic similarity measurement. Time Warping (DTW) and HMM (Hidden Markov Model) algorithm for fault classification task. These two techniques allow the direct use of data without the need for front ends for signal pre-processing, as well as being able to handle multivariate and variable time series, such as signal sequences for the post-miss case. To develop the two proposed systems for classification, simulated data of short-circuit faults from the UFPAFaults public database were used. To compare results with methodologies already presented in the literature for the problem, the FBSC architecture was also evaluated for the same database. In the case of FBSC architecture, different front ends and classifiers were used. The comparative assessment was performed from the measurement of error rate, computational cost and statistical tests. The results showed that the HMM-based classifier was more suitable for the problem of classification of short circuits on transmission lines.
Acesso aberto (Open Access)
Aplicação de técnicas estatísticas e de inteligência computacional na classificação de ciclos hidrológicos em reservatórios de água na região amazônica: um estudo de caso
(Universidade Federal do Pará, 2014-05-09) FREIRE, Jean Carlos Arouche; OLIVEIRA, Terezinha Ferreira de; http://lattes.cnpq.br/6230804143692945; MORAIS, Jefferson Magalhães de; http://lattes.cnpq.br/5219735119295290
This study evaluates the quality of the water reservoir of the Hydroelectric Plant Tucuruí according to the regional hydrological cycle and the spatial arrangement of the different sampling sites distributed in areas upstream of the dam in the period 2009-2012 from the amendment of 17 parameters physico-chemical and metals from water extracted of six factors that accounted for 71.01% of total data variability. It was observed that the greatest variations of NO3, NH4, , totalP, PO4 and STS occurred in the period of floods and may be an indication of trophic status in the sampling sites due to the existence of fishing poles or population density in the vicinity these sites. Discriminant analysis, artificial neural networks, k-nearest neighbors, support vector machine with polynomial and radial core and random forest: classification of the hydrological cycle to six classifiers were used. The results indicate that the random forest classifier showed the best performance with a percentage rating of 7.80% of incorrect predictions. While Student t test indicates that random forest and k-nearest neighbors have an average rate of incorrect predictions with equal significance index set at α = 5%.