Navegando por Assunto "Computational intelligence"

Agora exibindo 1 - 8 de 8

Acesso aberto (Open Access)
Análise dos impactos harmônicos em uma indústria de manufatura de eletroeletrônicos utilizando árvores de decisão
(Universidade Federal do Pará, 2015-03-27) NOGUEIRA, Rildo de Mendonça; SANTANA, Ádamo Lima de; http://lattes.cnpq.br/4073088744952858; TOSTES, Maria Emília de Lima; http://lattes.cnpq.br/4197618044519148
The Power Quality (PQ) is constantly the subject of many studies, mainly those that are related to the industrial production sector, where are concentrated large loads of the electrical systems. With the evolution of industrial production processes and the introduction of new technologies in the industrial sector, quantities of electronic equipment bars were added that are sources of disturbances in the systems, and affecting the quality of the product "electricity". In order to minimize the inconvenience resulting from low quality of energy and damage to utilities and consumers (industrial, commercial and residential), it was developed in Brazil, distribution procedures in the national grid (PRODIST), created and developed the National Electric Energy Agency (ANEEL). The PRODIST aims to regulate and standardize activities related to energy distribution, including product quality standards. This work was concentrated and held in a company of the industrial pole of Manaus (PIM), which has a three-phase electrical system low voltage, in order to monitor the quality of the product "electricity" through the harmonic content generated by the electrical network involved in manufacturing. The data generated were subjected to computational intelligence technique (HF), using the process of knowledge extraction discovery in databases or KDD. The objective is to analyze, identify and diagnose the coupling points and processes that have representative harmonic content for the system, so being able to check how much each analyzed process may be affecting the power quality within the industry itself and the point of coupling with the concessionaire, through the generations of harmonic distortion, thus avoiding penalties and other sanctions regulated.
Acesso aberto (Open Access)
Aplicação de sensores virtuais na inferência da temperatura de banho no processo de fabricação de alumínio primário
(Universidade Federal do Pará, 2009-12-14) SOARES, Fábio Mendes; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318
Nowadays, industries worldwide are looking forward to enlarge their profits and become more competitive. A good management is a key factor to accomplish the company’s target, however all management decisions are supported by tools that provide good and relevant information for the process, which usually influences decision making strategically. Soft Sensors have been applied in industries which are aiming that target and its use has been growing lately. A soft sensor can be adapted to any application regarding variable measurement, therefore reducing operational costs without compromising the current information quality, and in some cases, better results can be obtained. Since they are software based, they are not subjected to physical damages as real sensors are, so they can be adapted virtually to hostile environments. The key of this kind of sensor success is the use of computational intelligence techniques, which has been heavily used in nonlinear and highly complex process modeling. Currently, many industries already use them successfully, and this work exploits its use with Neural Networks in a chemical process in an important Brazilian Aluminum Smelter whose control is very hard to maintain once it is not easy to retrieve information from the plant due to its corrosive nature and whose measurements require some operational resources. The usage of soft sensors within it may reduce costs and delays of measures drastically. A case of use of the soft sensor for temperature measure is presented on this work, since its design through implementation at production, according to a researched methodology.
Acesso aberto (Open Access)
Avaliação da distorção harmônica total de tensão no ponto de acoplamento comum industrial usando o processo KDD baseado em medição
(Universidade Federal do Pará, 2018-03-27) OLIVEIRA, Edson Farias de; TOSTES, Maria Emília de Lima; http://lattes.cnpq.br/4197618044519148
In the last decades, the transformation industry has provided the introduction of increasingly faster and more energy efficient products for residential, commercial and industrial use, however these loads due to their non-linearity have contributed significantly to the increase of distortion levels harmonic of voltage as a result of the current according to the Power Quality indicators of the Brazilian electricity distribution system. The constant increase in the levels of distortions, especially at the point of common coupling, has generated in the current day a lot of concern in the concessionaires and in the consumers of electric power, due to the problems that cause like losses of the quality of electric power in the supply and in the installations of the consumers and this has provided several studies on the subject. In order to contribute to the subject, this thesis proposes a procedure based on the Knowledge Discovery in Database - KDD process to identify the impact loads of harmonic distortions of voltage at the common coupling point. The proposed methodology uses computational intelligence and data mining techniques to analyze the data collected by energy quality meters installed in the main loads and the common coupling point of the consumer and consequently establish the correlation between the harmonic currents of the nonlinear loads with the harmonic distortion at the common coupling point. The proposed process consists in analyzing the loads and the layout of the location where the methodology will be applied, in the choice and installation of the QEE meters and in the application of the complete KDD process, including the procedures for collection, selection, cleaning, integration, transformation and reduction, mining, interpretation, and evaluation of data. In order to contribute, the data mining techniques of Decision Tree and Naïve Bayes were applied and several algorithms were tested for the algorithm with the most significant results for this type of analysis as presented in the results. The results obtained evidenced that the KDD process has applicability in the analysis of the Voltage Total Harmonic Distortion at the Point of Common Coupling and leaves as contribution the complete description of each step of this process, and for this it was compared with different indices of data balancing, training and test and different scenarios in different shifts of analysis and presented good performance allowing their application in other types of consumers and energy distribution companies. It also shows, in the chosen application and using different scenarios, that the most impacting load was the seventh current harmonic of the air conditioning units for the collected data set.
Acesso aberto (Open Access)
Caracterização de padrões de descargas parciais em hidrogeradores utilizando técnicas de inteligência computacional
(Universidade Federal do Pará, 2015-09-24) ALVES, Medillin Pereira; NUNES, Marcus Vinícius Alves; http://lattes.cnpq.br/9533143193581447
This master's thesis presents the experiments with applications of computational intelligence techniques for the characterization of partial discharges in hydrogenerators. The classification of the partial discharge contributes to a prior analysis problems and allows predictive maintenance on machinery, reducing the possibility of failures in them. Data were collected online mode (operation machine) in the Tucuruí Hydroelectric Power Plant, observed the internal discharge standards, delamination and between bars. The IMA-DP software, developed in partnership with Eletronorte and Cepel, allowed these data were measured and recorded quickly, and organized through PRPD maps (Phase resolved Partial Discharges). Binarization techniques, ANOVA (Analisys of Variance), ICA (Independent Component Analysis) and PCA (Principal Component Analysis) were applied to the signals to adapt them to the use of computational intelligence techniques. The study was developed in IPython environment using scikit-learn library, which has efficient intelligence algorithms. The experiments were performed making use of techniques: KNN (K-Nearest Neighbors), Random Forest and MVS (Support Vector Machines). Such techniques showed good results with the experiments, highlighting those obtained for MVS that showed the best results, achieving an accuracy of 96.07%, due possess selection mechanisms of the main variables during the training process.
Acesso aberto (Open Access)
Estimação da porcentagem de flúor em alumina fluoretada proveniente de uma planta de tratamento de gases por meio de um sensor virtual neural
(Universidade Federal do Pará, 2011-06-22) SOUZA, Alan Marcel Fernandes de; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318; AFFONSO, Carolina de Mattos; http://lattes.cnpq.br/2228901515752720
The industries have been often seeking to reduce operating expenses, as to increase profits and competitiveness. To achieve this goal, it must take into account, among other factors, the design and implementation of new tools that accurately, efficiently and inexpensively allow access to information relevant to process. Soft sensors have been increasingly applied in industry. Since it offers flexibility, it can be adapted to make estimations of any measurement, thus a reducing in operating costs without compromising the measurements, and in some cases even improve the quality of generated information. Since they are completely softwarebased, they are not subjected to physical damage as the real sensors, and are better adaptated to harsh environments with hard access. The success of this king of sensors is due to the use of computational intelligence techniques, which have been widely used in the modeling of several nonlinear complex processes. This work aims to estimate the quality of alumina fluoride from a Gas Treatment Center (GTC), which is the result of gaseous adsorption on alumina virgin, using a soft sensor. The model that emulates the behavior of a alumina quality sensor the plant was created using an artificial intelligence technique known as Artificial Neural Network. The motivations of this work are: perform virtual simulations without compromising the GTC and make accurate decisions based not only on the operator's experience, to diagnose potential problems before they can interfere with the quality of alumina fluoride; maintain the aluminum reduction pot control variables within normal limits, since the production from low quality alumina strongly affects the reaction of breaking the molecule that contains this metal. The benefits this project brings include: increasing the GTC efficiency, producing high quality fluoridated alumina and emitting fewer greenhouse gases into the atmosphere and increasing the pot lifespan.
Acesso aberto (Open Access)
Metodologia para compressão de sinais de energia elétrica a partir de registros de forma de onda utilizando algorítmos genéticos e redes neurais artificiais
(Universidade Federal do Pará, 2016-12-16) BARROS, Fabíola Graziela Noronha; NUNES, Marcus Vinícius Alves; http://lattes.cnpq.br/9533143193581447; BEZERRA, Ubiratan Holanda; http://lattes.cnpq.br/6542769654042813
This thesis proposes a methodology for compression of electrical power signals from waveform records in electric systems, using genetic algorithm (GA) and artificial neural network (ANN).The genetic algorithm is used to select and preserve the points that better characterize the waveform contoursA and the artificial neural network is used in the compression of other points as well as on the signal reconstruction process. Thus, the data resulting are formed by a part of the original signal and by a compressed complementary part in the form of synaptic weights. The proposed methodology selects and preserves a percentage of the original signal samples, which are aspects not explored in the literature. The method was tested using field data obtained from an oscillographic recorder installed in a 230kV electrical power system. The results presented compression rates ranging from 88.36 to 95.86 for preservation rates ranging from 2.5 to 10 , respectively.
Acesso aberto (Open Access)
Modelagem neural da resistência elétrica dos fornos de redução do alumínio
(Universidade Federal do Pará, 2015-10-16) CONTE, Thiago Nicolau Magalhães de Souza; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318
The paper evaluates two types of Artificial Neural Networks to model dynamically the behaviour of the electrical resistance of a primary aluminum reduction furnace. The proposal is to use Direct Multilayer neural networks (RNMD) and Recurrent Neural networks (RNR) to model the electrical resistance of the oven. For each of these Neural Networks is explored its ability to model dynamic systems, either by varying the number of layers of neurons, as well as the number of neurons in each layer, varying the neural network input signals, etc. The data to be used in modeling from a Brazilian factory of primary aluminum. This modeling can be used to control the distance (up or down) between the electrodes, anodes and cathodes of the reduction that it consists primarily of carbonaceous materials. In this way the system of control has the task of maintaining the value of resistance within acceptable ranges of operation always attempting to ensure thermal stability and consequently the production of primary aluminum, high-purity, based on data available online in the control system of the plant. Through these electrodes are injected electrical currents keep that, besides the electrolysis itself cause the electrolytic bath, raising its temperature to a range up to 960° C. The motivation for the work is in high complexity of primary aluminum reduction process, whose nature is non-linear and the same suffering directly related variables influence the dynamics of the process, often imperceptible process engineers from the factory, but can be perceived by means of computational intelligence techniques reflecting about the different operating conditions of the real system.
Acesso aberto (Open Access)
Previsão de geração de energia fotovoltaica utilizando transformação de séries temporais em imagens e redes neurais convolucionais bidimensionais
(Universidade Federal do Pará, 2023-10-26) MONTEIRO, Diego Ramiro Melo; CASTRO, Adriana Rosa Garcez; http://lattes.cnpq.br/5273686389382860
This research presents a novel approach based on a Bidimensional Convolutional Neural Network (CNN) and techniques for transforming time series data into images, such as Gramian Angular Field (GAF) and Recurrence Plot (RP), for short-term forecast of electricity generation from a photovoltaic microgrid connected to the electrical grid, located at the Center of Excellence in Energy Efficiency of the Amazon (Centro de Excelência em Eficiência Energética da Amazônia –CEAMAZON) at the Federal University of Pará (Universidade Federal do Pará –UFPA). The GAF and RP techniques were employed to transform the time series data into images, which were used as input for the CNN. More accurate electricity generation forecasts enable users to better estimate the potential costs for grid implementation and the payback periods, as well as assess the available load capacity that can be connected to the system with higher precision. The prediction results using GAF and RP with a 2D CNN were compared with results obtained using other established neural network architectures in the field, such as Multilayer Perceptron and 1D CNNs, yielding satisfactory Root Mean Square Error (RMSE) values. This demonstrates the applicability of using images generated from the transformation of photovoltaic time series data in a 2D CNN for this problem.