Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems

One objective of the feeder reconfiguration problem in distribution systems is to minimize the power losses for a specific load. For this problem, mathematical modeling is a nonlinear mixed integer problem that is generally hard to solve. This paper proposes an algorithm based on artificial neural network theory. In this context, clustering techniques to determine the best training set for a single neural network with generalization ability are also presented. The proposed methodology was employed for solving two electrical systems and presented good results. Moreover, the methodology can be employed for large-scale systems in real-time environment.

Uma abordagem neural para estimação de conjugado em motores de indução

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The U-isotopes modeling in aquifers from Pocos de Caldas plateau, Brazil

U-modeled groundwater residence times were estimated by changes in U-234/U-238 activity ratio (AR) and U content of groundwaters from Pocos de Caldas city situated at the Pocos de Caldas alkaline massif, Brazil. The estimated ages are more realistic than others generated by the use of hydraulic conductivity data and available information about the weathering rate of rocks in the plateau. The U-234/U-238 AR and reciprocal of the dissolved U-content data also defined a ternary plot that allowed the calculation of the relative volume of three source waters for the mixed water. Such U-isotopes modeling for mixing calculations indicated that the dominant phase (68%) in the mixture is thermal.

Load torque estimation in induction motors using artificial neural networks

The induction motors are largely used in several industry sectors. The dimensioning of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this paper is to use artificial neural networks as tool for dimensioning of induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Simulation results are also presented to validate the proposed approach.

Predicting body and carcass characteristics of 2 broiler chicken strains using support vector regression and neural network models

As a new modeling method, support vector regression (SVR) has been regarded as the state-of-the-art technique for regression and approximation. In this study, the SVR models had been introduced and developed to predict body and carcass-related characteristics of 2 strains of broiler chicken. To evaluate the prediction ability of SVR models, we compared their performance with that of neural network (NN) models. Evaluation of the prediction accuracy of models was based on the R-2, MS error, and bias. The variables of interest as model output were BW, empty BW, carcass, breast, drumstick, thigh, and wing weight in 2 strains of Ross and Cobb chickens based on intake dietary nutrients, including ME (kcal/bird per week), CP, TSAA, and Lys, all as grams per bird per week. A data set composed of 64 measurements taken from each strain were used for this analysis, where 44 data lines were used for model training, whereas the remaining 20 lines were used to test the created models. The results of this study revealed that it is possible to satisfactorily estimate the BW and carcass parts of the broiler chickens via their dietary nutrient intake. Through statistical criteria used to evaluate the performance of the SVR and NN models, the overall results demonstrate that the discussed models can be effective for accurate prediction of the body and carcass-related characteristics investigated here. However, the SVR method achieved better accuracy and generalization than the NN method. This indicates that the new data mining technique (SVR model) can be used as an alternative modeling tool for NN models. However, further reevaluation of this algorithm in the future is suggested.

Application of ARX neural networks to model the rate of penetration of petroleum wells drilling

Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.

Artificial neural network model of discharge lamps in the power quality context

This paper presents a methodology for modeling high intensity discharge lamps based on artificial neural networks. The methodology provides a model which is able to represent the device operating in the frequency of distribution systems, facing events related to power quality. With the aid of a data acquisition system to monitor the laboratory experiment, and using $$\text{ MATLAB }^{\textregistered }$$ software, data was obtained for the training of two neural networks. These neural networks, working together, were able to represent with high fidelity the behavior of a discharge lamp. The excellent performance obtained by these models allowed the simulation of a group of lamps in a distribution system with shorter simulation time when compared to mathematical models. This fact justified the application of this family of loads in electric power systems. The representation of the device facing power quality disturbances also proved to be a useful tool for more complex studies in distribution systems. © 2013 Brazilian Society for Automatics - SBA.

Previsão de cargas elétricas através de uma Rede Neural Híbrida Back-ART Fuzzy

Pós-graduação em Engenharia Elétrica - FEIS

Análise da estabilidade estática de tensão de sistemas elétricos de potência usando uma rede neural baseada na teoria da ressonância adaptativa

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Previsão de carga multinodal usando a rede neural ART-ARTMAP fuzzy

Pós-graduação em Engenharia Elétrica - FEIS

Estimação da porcentagem de flúor em alumina fluoretada proveniente de uma planta de tratamento de gases por meio de um sensor virtual neural

As indústrias têm buscado constantemente reduzir gastos operacionais, visando o aumento do lucro e da competitividade. Para alcançar essa meta, são necessários, dentre outros fatores, o projeto e a implantação de novas ferramentas que permitam o acesso às informações relevantes do processo de forma precisa, eficiente e barata. Os sensores virtuais têm sido aplicados cada vez mais nas indústrias. Por ser flexível, ele pode ser adaptado a qualquer tipo de medição, promovendo uma redução de custos operacionais sem comprometer, e em alguns casos até melhorar, a qualidade da informação gerada. Como estão totalmente baseados em software, não estão sujeitos a danos físicos como os sensores reais, além de permitirem uma melhor adaptação a ambientes hostis e de difícil acesso. A razão do sucesso destes tipos de sensores é a utilização de técnicas de inteligência computacional, as quais têm sido usadas na modelagem de vários processos não lineares altamente complexos. Este trabalho tem como objetivo estimar a qualidade da alumina fluoretada proveniente de uma Planta de Tratamento de Gases (PTG), a qual é resultado da adsorção de gases poluentes em alumina virgem, via sensor virtual. O modelo que emula o comportamento de um sensor de qualidade de alumina foi criado através da técnica de inteligência computacional conhecida como Rede Neural Artificial. As motivações deste trabalho consistem em: realizar simulações virtuais, sem comprometer o funcionamento da PTG; tomar decisões mais precisas e não baseada somente na experiência do operador; diagnosticar potenciais problemas, antes que esses interfiram na qualidade da alumina fluoretada; manter o funcionamento do forno de redução de alumínio dentro da normalidade, pois a produção de alumina de baixa qualidade afeta a reação de quebra da molécula que contém este metal. Os benefícios que este projeto trará consistem em: aumentar a eficiência da PTG, produzindo alumina fluoretada de alta qualidade e emitindo menos gases poluentes na atmosfera, além de aumentar o tempo de vida útil do forno de redução.

Contribuições na modelagem dinâmica e no controle neural de um braço robótico com elos flexíveis

Em geral, estruturas espaciais e manipuladores robóticos leves têm uma característica similar e inerente que é a flexibilidade. Esta característica torna a dinâmica do sistema muito mais complexa e com maiores dificuldades para a análise de estabilidade e controle. Então, braços robóticos bastantes leves, com velocidade elevada e potencia limitada devem considerar o controle de vibração causada pela flexibilidade. Por este motivo, uma estratégia de controle é desejada não somente para o controle do modo rígido mas também que seja capaz de controlar os modos de vibração do braço robótico flexível. Também, redes neurais artificiais (RNA) são identificadas como uma subespecialidade de inteligência artificial. Constituem atualmente uma teoria para o estudo de fenômenos complexos e representam uma nova ferramenta na tecnologia de processamento de informação, por possuírem características como processamento paralelo, capacidade de aprendizagem, mapeamento não-linear e capacidade de generalização. Assim, neste estudo utilizam-se RNA na identificação e controle do braço robótico com elos flexíveis. Esta tese apresenta a modelagem dinâmica de braços robóticos com elos flexíveis, 1D no plano horizontal e 2D no plano vertical com ação da gravidade, respectivamente. Modelos dinâmicos reduzidos são obtidos pelo formalismo de Newton-Euler, e utiliza-se o método dos elementos finitos (MEF) na discretização dos deslocamentos elásticos baseado na teoria elementar da viga. Além disso, duas estratégias de controle têm sido desenvolvidas com a finalidade de eliminar as vibrações devido à flexibilidade do braço robótico com elos flexíveis. Primeiro, utilizase um controlador neural feedforward (NFF) na obtenção da dinâmica inversa do braço robótico flexível e o calculo do torque da junta. E segundo, para obter precisão no posicionamento... (Resumo completo, clicar acesso eletrônico abaixo)

Graded meshes in bio-thermal problems with transmission-line modeling method

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Modeling Age-Specific Mortality For Marine Mammal Populations

A method is presented for estimating age-specific mortality based on minimal information: a model life table and an estimate of longevity. This approach uses expected patterns of mammalian survivorship to define a general model of age-specific mortality rates. One such model life table is based on data for northern fur seals (Callorhinus ursinus) using Siler’s (1979) 5-parameter competing risk model. Alternative model life tables are based on historical data for human females and on a published model for Old World monkeys. Survival rates for a marine mammal species are then calculated by scaling these models by the longevity of that species. By using a realistic model (instead of assuming constant mortality), one can see more easily the real biological limits to population growth. The mortality estimation procedure is illustrated with examples of spotted dolphins (Stenella attenuata) and harbor porpoise (Phocoena phocoena).

Feature-oriented regional modeling and simulations (FORMS) for the western South Atlantic: Southeastern Brazil region

The multi-scale synoptic circulation system in the southeastern Brazil (SEBRA) region is presented using a feature-oriented approach. Prevalent synoptic circulation structures, or ""features,"" are identified from previous observational studies. These features include the southward-flowing Brazil Current (BC), the eddies off Cabo Sao Tome (CST - 22 degrees S) and off Cabo Frio (CF - 23 degrees S), and the upwelling region off CF and CST. Their synoptic water-mass (T-S) structures are characterized and parameterized to develop temperature-salinity (T-S) feature models. Following [Gangopadhyay, A., Robinson, A.R., Haley, PJ., Leslie, W.J., Lozano, C.j., Bisagni, J., Yu, Z., 2003. Feature-oriented regional modeling and simulation (forms) in the gulf of maine and georges bank. Cont. Shelf Res. 23 (3-4), 317-353] methodology, a synoptic initialization scheme for feature-oriented regional modeling and simulation (FORMS) of the circulation in this region is then developed. First, the temperature and salinity feature-model profiles are placed on a regional circulation template and objectively analyzed with available background climatology in the deep region. These initialization fields are then used for dynamical simulations via the Princeton Ocean Model (POM). A few first applications of this methodology are presented in this paper. These include the BC meandering, the BC-eddy interaction and the meander-eddy-upwelling system (MEUS) simulations. Preliminary validation results include realistic wave-growth and eddy formation and sustained upwelling. Our future plan includes the application of these feature models with satellite, in-situ data and advanced data-assimilation schemes for nowcasting and forecasting the SEBRA region. (c) 2008 Elsevier Ltd. All rights reserved.