A computational tool to assist the analysis of the transformer behavior related to lightning

This paper proposes the application of computational intelligence techniques to assist complex problems concerning lightning in transformers. In order to estimate the currents related to lightning in a transformer, a neural tool is presented. ATP has generated the training vectors. The input variables used in Artificial Neural Networks (ANN) were the wave front time, the wave tail time, the voltage variation rate and the output variable is the maximum current in the secondary of the transformer. These parameters can define the behavior and severity of lightning. Based on these concepts and from the results obtained, it can be verified that the overvoltages at the secondary of transformer are also affected by the discharge waveform in a similar way to the primary side. By using the tool developed, the high voltage process in the distribution transformers can be mapped and estimated with more precision aiding the transformer project process, minimizing empirics and evaluation errors, and contributing to minimize the failure rate of transformers. (C) 2011 Elsevier Ltd. All rights reserved.

LMI-based algorithm for strictly positive real systems with static output feedback

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

Estudo comparativo de estratégias de controle aplicadas a um gerador síncrono

The robustness and performance of the Variable Structure Adaptive Pole Placement Controller are evaluated in this work, where this controller is applied to control a synchronous generator connected to an infinite bus. The evaluation of the robustness of this controller will be accomplished through simulations, where the control algorithm was subjected to adverse conditions, such as: disturbances, parametric variations and unmodeled dynamic. It was also made a comparison of this control strategy with another one, using classic controllers. In the simulations, it is used a coupled model of the synchronous generator which variables have a high degree of coupling, in other words, if there is a change in the input variables of the generator, it will change all outputs simultaneously. The simulation results show which control strategy performs better and is more robust to disturbances, parametric variations and unmodeled dynamics for the control of Synchronous Generator

A comparison of global estimates of marine primary production from ocean color

The third primary production algorithm round robin (PPARR3) compares output from 24 models that estimate depth-integrated primary production from satellite measurements of ocean color, as well as seven general circulation models (GCMs) coupled with ecosystem or biogeochemical models. Here we compare the global primary production fields corresponding to eight months of 1998 and 1999 as estimated from common input fields of photosynthetically-available radiation (PAR), sea-surface temperature (SST), mixed-layer depth, and chlorophyll concentration. We also quantify the sensitivity of the ocean-color-based models to perturbations in their input variables. The pair-wise correlation between ocean-color models was used to cluster them into groups or related output, which reflect the regions and environmental conditions under which they respond differently. The groups do not follow model complexity with regards to wavelength or depth dependence, though they are related to the manner in which temperature is used to parameterize photosynthesis. Global average PP varies by a factor of two between models. The models diverged the most for the Southern Ocean, SST under 10 degrees C, and chlorophyll concentration exceeding 1 mg Chlm(-3). Based on the conditions under which the model results diverge most, we conclude that current ocean-color-based models are challenged by high-nutrient low-chlorophyll conditions, and extreme temperatures or chlorophyll concentrations. The GCM-based models predict comparable primary production to those based on ocean color: they estimate higher values in the Southern Ocean, at low SST, and in the equatorial band, while they estimate lower values in eutrophic regions (probably because the area of high chlorophyll concentrations is smaller in the GCMs). Further progress in primary production modeling requires improved understanding of the effect of temperature on photosynthesis and better parameterization of the maximum photosynthetic rate. (c) 2006 Elsevier Ltd. All rights reserved.

Assessing the uncertainties of model estimates of primary productivity in the tropical Pacific Ocean

Depth-integrated primary productivity (PP) estimates obtained from satellite ocean color-based models (SatPPMs) and those generated from biogeochemical ocean general circulation models (BCGCMs) represent a key resource for biogeochemical and ecological studies at global as well as regional scales. Calibration and validation of these PP models are not straightforward, however, and comparative studies show large differences between model estimates. The goal of this paper is to compare PP estimates obtained from 30 different models (21 SatPPMs and 9 BOGCMs) to a tropical Pacific PP database consisting of similar to 1000 C-14 measurements spanning more than a decade (1983-1996). Primary findings include: skill varied significantly between models, but performance was not a function of model complexity or type (i.e. SatPPM vs. BOGCM); nearly all models underestimated the observed variance of PR specifically yielding too few low PP (< 0.2 g Cm-2 d(-1)) values; more than half of the total root-mean-squared model-data differences associated with the satellite-based PP models might be accounted for by uncertainties in the input variables and/or the PP data; and the tropical Pacific database captures a broad scale shift from low biomassnormalized productivity in the 1980s to higher biomass-normalized productivity in the 1990s, which was not successfully captured by any of the models. This latter result suggests that interdecadal and global changes will be a significant challenge for both SatPPMs and BOGCMs. Finally, average root-mean-squared differences between in situ PP data on the equator at 140 degrees W and PP estimates from the satellite-based productivity models were 58% lower than analogous values computed in a previous PP model comparison 6 years ago. The success of these types of comparison exercises is illustrated by the continual modification and improvement of the participating models and the resulting increase in model skill. (C) 2008 Elsevier BY. All rights reserved.

An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across the globe

Nearly half of the earth's photosynthetically fixed carbon derives from the oceans. To determine global and region specific rates, we rely on models that estimate marine net primary productivity (NPP) thus it is essential that these models are evaluated to determine their accuracy. Here we assessed the skill of 21 ocean color models by comparing their estimates of depth-integrated NPP to 1156 in situ C-14 measurements encompassing ten marine regions including the Sargasso Sea, pelagic North Atlantic, coastal Northeast Atlantic, Black Sea, Mediterranean Sea, Arabian Sea, subtropical North Pacific, Ross Sea, West Antarctic Peninsula, and the Antarctic Polar Frontal Zone. Average model skill, as determined by root-mean square difference calculations, was lowest in the Black and Mediterranean Seas, highest in the pelagic North Atlantic and the Antarctic Polar Frontal Zone, and intermediate in the other six regions. The maximum fraction of model skill that may be attributable to uncertainties in both the input variables and in situ NPP measurements was nearly 72%. on average, the simplest depth/wavelength integrated models performed no worse than the more complex depth/wavelength resolved models. Ocean color models were not highly challenged in extreme conditions of surface chlorophyll-a and sea surface temperature, nor in high-nitrate low-chlorophyll waters. Water column depth was the primary influence on ocean color model performance such that average skill was significantly higher at depths greater than 250 m, suggesting that ocean color models are more challenged in Case-2 waters (coastal) than in Case-1 (pelagic) waters. Given that in situ chlorophyll-a data was used as input data, algorithm improvement is required to eliminate the poor performance of ocean color NPP models in Case-2 waters that are close to coastlines. Finally, ocean color chlorophyll-a algorithms are challenged by optically complex Case-2 waters, thus using satellite-derived chlorophyll-a to estimate NPP in coastal areas would likely further reduce the skill of ocean color models.

A neural approach to evaluate the effect of lightning in power transformers

This paper proposes the application of computational intelligence techniques to assist complex problems concerning lightning in transformers. In order to estimate the currents related to lightning in a transformer, a neural tool is presented. ATP has generated the training vectors. The input variables used in Artificial Neural Networks (ANN) were the wave front time, the wave tail time, the voltage variation rate and the output variable is the maximum current in the secondary of the transformer. These parameters can define the behavior and severity of lightning. Based on these concepts and from the results obtained, it can be verified that the overvoltages at the secondary of transformer are also affected by the discharge waveform in a similar way to the primary side. By using the tool developed, the high voltage process in the distribution transformers can be mapped and estimated with more precision aiding the transformer project process, minimizing empirics and evaluation errors, and contributing to minimize the failure rate of transformers. © 2009 The Berkeley Electronic Press. All rights reserved.

Analysis of polymerization time on abrasive wear of dental resins

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

Previsão de carga multinodal utilizando redes neurais de regressão generalizada

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estimativa da radiação solar ultravioleta em Botucatu/SP Brasil utilizando técnicas de aprendizado de máquina

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Análise do processo de lixamento tubular da madeira de Corymbia citriodora

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estimando o desfecho no recém-nascido grave através da Lógica Fuzzy

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aplicação de redes neurais no controle de teores de cobre e ouro do depósito de Chapada (GO)

Pós-graduação em Geologia Regional - IGCE

Avaliação econômica de Eucalipto irrigado em diferentes cenários

Pós-graduação em Agronomia (Irrigação e Drenagem) - FCA

Avaliação das condições de secagem no rendimento e na qualidade do óleo de linhaça (Linum usitatissimum L.)

De origem asiática, a semente de linhaça (Linum usitatissimum L.) pertence à família das Lináceas e é obtida a partir do linho. A semente da linhaça é ainda a maior fonte alimentar de lignanas, compostos fotoquímicos parecidos com o estrogênio, que podem desempenhar ação anticancerígena. Rica em fibras solúveis tem aproximadamente 40% do seu peso composto por óleos ricos em Ômega 3, entre os quais se destaca o α-linolênico. A secagem é a operação unitária segundo o qual ocorre eliminação da água por evaporação ou sublimação, presente em um material, mediante a aplicação de calor com condições controladas. Visando averiguar o comportamento das sementes de linhaça durante a operação de secagem, o presente trabalho teve como objetivo principal realizar o planejamento experimental e analisar estatísticamente os resultados empregados para quantificar a influência da temperatura do ar (T), tempo de secagem (t), velocidade do ar de fluidização (Uf) e carga de sólidos (Cs), sobre a razão de umidade (Xr), rendimento em óleo (Rend.) e os parâmetros oleoquimicos. A estimativa do ponto ótimo de operação foi determinada em função das variáveis de entrada aplicando o conceito de desejabilidade global. Dentre as condições estabelecidas neste trabalho, o valor ótimo da Função Desejabilidade é quando T é deslocada próximo ao nível alto (72 oC), t para o mínimo (3 h), Uf para o ponto próximo ao central (0,83 m/s) e a Cs para o nível alto (500g), obtendo-se assim: 0,126 para Xr; 36,92 % para Rend.; 4,51 mg KOH/g para IA; 22,52 meqO2/Kg IP e 0,31% para DC. Foram obtidas as isotermas de dessorção das sementes de linhaça nas temperaturas de 40, 60 e 80°C. Os dados experimentais foram avaliados usando seis modelos matemáticos. A entalpia e a entropia diferencial de dessorção foram estimadas por meio das relações de Clausius-Clapeyron e Gibbs-Helmholtz, respectivamente. Os modelos de GAB e Peleg ajustaram adequadamente os dados experimentais. A teoria da compensação entalpia-entropia foi aplicada com sucesso às isotermas de dessorção e indica que o mecanismo de dessorção de umidade das sementes de linhaça pode ser considerado como controlado pela entalpia. A secagem das sementes de linhaça previamente umidificadas foram avaliadas em um secador de leito fixo e fluidizado, as corridas experimentais foram realizadas nas temperaturas de 40, 60 e 80°C, dentre dos cinco modelos propostos, o modelo de Midilli et al, foi o melhor modelo que melhor ajustou aos dados experimentais. Foi observado que a difusividade efetiva para as sementes de linhaça aumentou com a elevação da temperatura do ar de secagem para a secagem em leito fixo e fluidizado. A dependência da difusividade em relação à temperatura foi descrita pela equação de Arrhenius, por meio da qual se estimou para ambos os processos de secagem.