Contribuições para a detecção e identificação de faltas em inversores Multiníveis

This work presents contributions in the detection and identication of faults in multilevel inverters through the study of the converters behavior under these operation conditions. Basically, the approached fault consists of an open-circuit in any switch of a three-level clamped diode inverter. The converter operation is characterized in the pre and post-fault states. A wave form behavior analysis of the pole voltage, phase current and dc-bus current is also done, which highlights characteristics that allow the detection of failure and, even, under favorable conditions, the identication of the faulty device. A compensation strategy of the approached fault (open-switch) is also investigated with the purpose of maintaining the driving system operational when a failure occurs. The proposed topology uses SCRs in parallel with the internal switches of the inverter, which allows, in some occasions, the full utilization of the dc-bus

Detecção e diagnóstico de falhas não-supervisionados baseados em estimativa de densidade recursiva e classificador fuzzy auto-evolutivo

In this work, we propose a two-stage algorithm for real-time fault detection and identification of industrial plants. Our proposal is based on the analysis of selected features using recursive density estimation and a new evolving classifier algorithm. More specifically, the proposed approach for the detection stage is based on the concept of density in the data space, which is not the same as probability density function, but is a very useful measure for abnormality/outliers detection. This density can be expressed by a Cauchy function and can be calculated recursively, which makes it memory and computational power efficient and, therefore, suitable for on-line applications. The identification/diagnosis stage is based on a self-developing (evolving) fuzzy rule-based classifier system proposed in this work, called AutoClass. An important property of AutoClass is that it can start learning from scratch". Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for AutoClass (the number may grow, with new class labels being added by the on-line learning process), in a fully unsupervised manner. In the event that an initial rule base exists, AutoClass can evolve/develop it further based on the newly arrived faulty state data. In order to validate our proposal, we present experimental results from a level control didactic process, where control and error signals are used as features for the fault detection and identification systems, but the approach is generic and the number of features can be significant due to the computationally lean methodology, since covariance or more complex calculations, as well as storage of old data, are not required. The obtained results are significantly better than the traditional approaches used for comparison

Detecção de áreas degradadas na sub-bacia hidrográfica do rio taperoá/pb, utilizando parâmetros físicos dos sensores modis/terra e tm/landsat

This study includes the results of the analysis of areas susceptible to degradation by remote sensing in semi-arid region, which is a matter of concern and affects the whole population and the catalyst of this process occurs by the deforestation of the savanna and improper practices by the use of soil. The objective of this research is to use biophysical parameters of the MODIS / Terra and images TM/Landsat-5 to determine areas susceptible to degradation in semi-arid Paraiba. The study area is located in the central interior of Paraíba, in the sub-basin of the River Taperoá, with average annual rainfall below 400 mm and average annual temperature of 28 ° C. To draw up the map of vegetation were used TM/Landsat-5 images, specifically, the composition 5R4G3B colored, commonly used for mapping land use. This map was produced by unsupervised classification by maximum likelihood. The legend corresponds to the following targets: savanna vegetation sparse and dense, riparian vegetation and exposed soil. The biophysical parameters used in the MODIS were emissivity, albedo and vegetation index for NDVI (NDVI). The GIS computer programs used were Modis Reprojections Tools and System Information Processing Georeferenced (SPRING), which was set up and worked the bank of information from sensors MODIS and TM and ArcGIS software for making maps more customizable. Initially, we evaluated the behavior of the vegetation emissivity by adapting equation Bastiaanssen on NDVI for spatialize emissivity and observe changes during the year 2006. The albedo was used to view your percentage of increase in the periods December 2003 and 2004. The image sensor of Landsat TM were used for the month of December 2005, according to the availability of images and in periods of low emissivity. For these applications were made in language programs for GIS Algebraic Space (LEGAL), which is a routine programming SPRING, which allows you to perform various types of algebras of spatial data and maps. For the detection of areas susceptible to environmental degradation took into account the behavior of the emissivity of the savanna that showed seasonal coinciding with the rainy season, reaching a maximum emissivity in the months April to July and in the remaining months of a low emissivity . With the images of the albedo of December 2003 and 2004, it was verified the percentage increase, which allowed the generation of two distinct classes: areas with increased variation percentage of 1 to 11.6% and the percentage change in areas with less than 1 % albedo. It was then possible to generate the map of susceptibility to environmental degradation, with the intersection of the class of exposed soil with varying percentage of the albedo, resulting in classes susceptibility to environmental degradation

Potencialidades analíticas do dietilditiofosfato de amônio em espectrometria de massas com plasma indutivamente acoplado empregando extração em fase sólida e sistemas de injeção em fluxo

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Detecção de falhas em estruturas inteligentes usando otimização por nuvem de partículas: fundamentos e estudo de casos

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

Detecção de áreas de oclusão para geração de ortoimagem verdadeira utilizando dados LASER

Pós-graduação em Ciências Cartográficas - FCT

Aplicação de um Modelo de Difusão Anisotrópica e Morfologia Matemática na Detecção de Feições Cartográficas

Pós-graduação em Ciências Cartográficas - FCT

Otimização de processos semi-automáticos de detecção de alterações aplicadas ao cadastro imobiliário

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Um método para detecção e classificação de curtos-circuitos em redes de distribuição de energia elétrica baseado na transformada de Fourier e em redes neurais artificiais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Desenvolvimento e implementação de um sistema para detecção de falhas em estruturas usando microcontrolador

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Algoritmos de detecção de bordas implementados em FPGA

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Sistema inteligente híbrido intercomunicativo para detecção de perdas comerciais

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multicontroladores para melhoria da robustez e acomodação de falhas em sistemas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise de distúrbios de tensão em sistemas de distribuição de energia elétrica baseada em sistemas imunológicos artificiais

Pós-graduação em Engenharia Elétrica - FEIS

Teoria da ressonância adaptativa através da linguagem Java para detecção e classificação de e-mails indesejados

Pós-graduação em Engenharia Elétrica - FEIS