Este trabalho consiste em apresentar o desenvolvimento de um Smart meter, integrado a um analisador de qualidade de energia, para análise de distorções harmônicas, utilizando método de redes neurais artificiais embarcado em hardware. Tal Smart meter está incluído dentro dos conceitos de Smart Grid, que serão apresentados também neste trabalho. O intuito do desenvolvimento do Smart meter para análise de distorções harmônicas é auxiliar concessionárias de energia elétrica a identificar que tipo de carga o consumidor utiliza em sua residência, a fim de contribuir para a tomada de decisões apropriadas, tais como a diminuição da emissão de correntes harmônicas, demanda de energia, detecção de falhas no fornecimento de energia elétrica e faturas diferenciadas de acordo com a quantidade de harmônicas injetadas na rede elétrica. Adicionalmente, observou-se que o Smart meter desenvolvido pode ser ainda utilizado para detectar fenômenos de VTCD, como elevação, afundamento e interrupção de energia. Todo o processo de desenvolvimento do Smart meter é apresentado no decorrer desta tese de doutorado. / This thesis consists to present the development of a Smart Meter integrated to power quality analyzer for the analysis of harmonic distortion, using methods based on artificial neural networks in embedded hardware. This Smart Meter is included within the concepts of Smart Grid, which will be also presented in this work. The intention of the development of the Smart Meter for analysis of harmonic distortion is to assist utilities companies to identify what loads type the consumer uses at your residence in order to contribute for supporting decisions, such as reducing the emission of the harmonic currents, power demand and faults detection in electric energy supply and distinct bills according to the amount of harmonics injected into the power grid. In addition, it was observed that this developed Smart Meter can be even used to detect the VTCD phenomena, such as swell, sag and interruption of the energy supply. All development steps of this Smart Meter is presented in this doctoral thesis.

The Singular Value Decomposition (SVD) has many applications in image processing. The SVD can be used to restore a corrupted image by separating significant information from the noise in the image data set. This thesis outlines broad applications that address current problems in digital image processing. In conjunction with SVD filtering, image compression using the SVD is discussed, including the process of reconstructing or estimating a rank reduced matrix representing the compressed image. Numerical plots and error measurement calculations are used to compare results of the two SVD image restoration techniques, as well as SVD image compression. The filtering methods assume that the images have been degraded by the application of a blurring function and the addition of noise. Finally, we present numerical experiments for the SVD restoration and compression to evaluate our computation.

Orientador: Jozué Vieira Filho / Banca: Carlos Roberto Minussi / Banca: Walmir de Freitas Filho / Resumo: Neste trabalho apresenta-se uma Metodologia para Detecção e Classificação de Curtos-Circuitos em alimentadores de Sistemas de Distribuição de Energia Elétrica fundamentada na análise de registros oscilográficos através da DFT (Discrete Fourier Transform) e de RNAs (Redes Neurais Artificiais). Sua aplicação pressupõe a disponibilidade de registros de oscilografia digital das correntes nas três fases do alimentador, monitoradas apenas na saída da subestação. A caracterização de cada tipo de curto-circuito é obtida mediante a análise do comportamento dinâmico das correntes de fase durante o período transitório das faltas e a detecção e classificação dos curtos-circuitos são efetuadas por meio de um banco de RNAs acíclicas, do tipo perceptrons, de múltiplas camadas. Um modelo de um alimentador real de Sistema de Distribuição de grande porte, composto por 836 barras, foi utilizado na obtenção dos dados referentes aos curtos-circuitos, com simulações via software ATP-EMTP (Alternative Transients Program - Electromagnetic Transients Program). O método foi implementado e testado utilizando-se o software MATLAB®. Como resultado tem-se uma metodologia de formulação simples que apresenta bom desempenho, é de fácil implementação, apresenta baixa carga computacional e gera resultados altamente satisfatórios / Abstract: This work proposes a methodology for Detection and Classification of Short-Circuits in Distribution Electric Power feeders, based on the analysis of oscillograph records through the application of DFT (Discrete Fourier Transform) and ANNs (Artificial Neural Networks). Its application requires the availability of digital oscillograph records of the currents in the three phases of the feeder, only monitored at the output of the substation. The characterization of each type of short-circuit is obtained by means of analysis of the dynamic behavior of the phase currents during the transitory period. The detection and classification of short-circuits is performed by a bank of acyclic ANNs, of type multilayers perceptrons. A real feeder model of a large distribution power system, composed of 836 buses, was used to obtain data relating to short-circuits, simulated via ATP-EMTP (Alternative Transients Program - Electromagnetic Transients Program) software. The method was implemented and tested using MATLAB®. As a conclusion, the proposed method is simple to be implemented, presents low computational load and generates good results / Mestre

Orientadores: Sigmar Maurer Deckmann, Fernando Pinhabel Marafão / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-07T20:09:32Z (GMT). No. of bitstreams: 1 Padua_MarceloSuzartde_M.pdf: 2971342 bytes, checksum: f838d91f23e156c63fa9582c9c5c582e (MD5) Previous issue date: 2006 / Resumo: Este trabalho visa contribuir para os estudos sobre sincronismo em um Sistema Elétrico de Potência (SEP), com enfoque em Geração Distribuída (GD). Buscar-se-á analisar as condições para operação de um gerador distribuído em paralelo com a rede elétrica, bem como desenvolver metodologias de sincronismo para tal. Muitos algoritmos propostos para aplicações em Eletrônica de Potência e Qualidade da Energia Elétrica podem ser utilizados para este propósito. Inicialmente, será apresentadas uma síntese dos tipos de geração alternativa existentes e suas características. Considerando que o Brasil, até o momento, não possui legislação específica referente à GD, serão expostas as principais normas internacionais aplicáveis ao sincronismo e à conexão do gerador, além de aspectos de segurança, como condições de ilhamento. Cada um dos algoritmos escolhidos (PLL, TDFR e FK) será estudado com o objetivo de observar sua resposta dinâmica, precisão em regime e robustez na presença de distorções harmônicas e/ou transitórios nas tensões da rede, de acordo com o ajuste dos seus parâmetros. Finalmente, serão apresentados resultados de simulação de um sistema de GD característico. Uma carga, alimentada pela rede elétrica de distribuição, passará a receber energia também de um gerador local, que será conectado em paralelo com esta rede com auxílio dos algoritmos de sincronismo estudados. Resultados experimentais confirmarão a possibilidade de implementação dessas técnicas / Abstract: This dissertation presents a contribution to the study of synchronization in Electric Power Systems, focusing Distributed Generation. The conditions for operation of a distributed generator in parallel with the grid will be analyzed and applicable methodologies of synchronism will be presented. Different algorithms proposed for Power Electronics Applications and Power Quality Analysis can be used for this intention. Initially, this work presents a synthesis of the existing types of alternative generation and their characteristics. Considering that in Brazil, up to this moment, does not exist a specific legislation for distributed generation, the international main applicable norms about synchronization and connection of the generator will be exposed, including security aspects, such as islanding conditions. Each of the chosen algorithms will be studied to observe its dynamic response, accuracy and robustness in presence of harmonic distortions or transients in the utility voltage, according to the adjusted parameters. Finally, simulation results of a characteristic industrial plant will be presented. The utility system supplies a load, which will draw power also from a local generator, connected to this grid, with the aid of the studied synchronism algorithms. Experimental results will confirm the possibility of implementation of these techniques / Mestrado / Energia Eletrica / Mestre em Engenharia Elétrica

Este trabalho consiste em apresentar o desenvolvimento de um Smart meter, integrado a um analisador de qualidade de energia, para análise de distorções harmônicas, utilizando método de redes neurais artificiais embarcado em hardware. Tal Smart meter está incluído dentro dos conceitos de Smart Grid, que serão apresentados também neste trabalho. O intuito do desenvolvimento do Smart meter para análise de distorções harmônicas é auxiliar concessionárias de energia elétrica a identificar que tipo de carga o consumidor utiliza em sua residência, a fim de contribuir para a tomada de decisões apropriadas, tais como a diminuição da emissão de correntes harmônicas, demanda de energia, detecção de falhas no fornecimento de energia elétrica e faturas diferenciadas de acordo com a quantidade de harmônicas injetadas na rede elétrica. Adicionalmente, observou-se que o Smart meter desenvolvido pode ser ainda utilizado para detectar fenômenos de VTCD, como elevação, afundamento e interrupção de energia. Todo o processo de desenvolvimento do Smart meter é apresentado no decorrer desta tese de doutorado. / This thesis consists to present the development of a Smart Meter integrated to power quality analyzer for the analysis of harmonic distortion, using methods based on artificial neural networks in embedded hardware. This Smart Meter is included within the concepts of Smart Grid, which will be also presented in this work. The intention of the development of the Smart Meter for analysis of harmonic distortion is to assist utilities companies to identify what loads type the consumer uses at your residence in order to contribute for supporting decisions, such as reducing the emission of the harmonic currents, power demand and faults detection in electric energy supply and distinct bills according to the amount of harmonics injected into the power grid. In addition, it was observed that this developed Smart Meter can be even used to detect the VTCD phenomena, such as swell, sag and interruption of the energy supply. All development steps of this Smart Meter is presented in this doctoral thesis.

Energy storage systems will likely be needed for future shipboard power systems that supply loads with high power variability such as pulsed power loads. The power generation in shipboard power systems may not be sufficient to satisfy the energy demands of the pulsed power load systems operating in conjunction with other ship service loads. Two fundamental items in evaluating the requirements of an energy storage system are the energy storage capacity and the ratings of the power conversion equipment that interfaces the energy device to the power system. The supply current of pulsed power load systems is aperiodic and cannot be described in terms of active power. Also, the RMS value and thus apparent power are only defined for periodic quantities. Therefore traditional methods of rating power equipment cannot be used. This thesis describes an approach to determine the ratings of an energy storage interface and the energy storage capacity of an energy storage device as a function of load and supply parameters. The results obtained using the proposed approach are validated with the results obtained from the simulation model of the generator supplying a pulsed power load in conjunction with an energy storage system. The energy storage system requirements for various pulsed power load profiles are obtained using the proposed approach. The method used for determining the ratings of an energy storage system utilizes an orthogonal decomposition of pulsed power load system supply current evaluated within a sliding window. The signals obtained from the decomposition are also useful in generating the control reference signals for the energy storage interface. Although the approach and methods are focused on a particular structure of the pulsed power load system, they may be generalized for use in any type of configuration of a pulsed power load system.

No description available.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-04-24T15:28:35Z No. of bitstreams: 1 paolajohanasaboyalopez.pdf: 1042873 bytes, checksum: a46dd95de00e062cba39ef4b9b642462 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-04-24T17:09:24Z (GMT) No. of bitstreams: 1 paolajohanasaboyalopez.pdf: 1042873 bytes, checksum: a46dd95de00e062cba39ef4b9b642462 (MD5) / Made available in DSpace on 2017-04-24T17:09:24Z (GMT). No. of bitstreams: 1 paolajohanasaboyalopez.pdf: 1042873 bytes, checksum: a46dd95de00e062cba39ef4b9b642462 (MD5) Previous issue date: 2013-06-03 / A presente dissertação tem por objetivo propor e avaliar cinco técnicas de detecção de ruídos impulsivos para a melhoria da transmissão digital de dados via redes de energia elétrica (do inglês, Power Line Communications) (PLC). As técnicas propostas contemplam a detecção de ruídos impulsivos no domínio do tempo discreto, no domínio da transformada wavelet discreta (do inglês, Discrete Wavelet Transform) (DWT) e no domínio da transformada discreta de Fourier (do inglês, Discrete Fourier Transform) (DFT). Tais técnicas fazem uso de métodos de extração e seleção de características, assim como métodos de detecção de sinais baseados na teoria de Bayes e redes neurais. Análises comparativas explicitam as vantagens e desvantagens de cada uma das técnicas propostas para o problema em questão, e ainda indicam que estas são bastante adequadas para a solução do mesmo. / This dissertation aims to propose and evaluate five techniques for impulsive noise detection in order to improve digital communications through power line channels. The imput signals for the proposed detection techniques are impulsive noise signals on discrete-time domain, on the Discrete Wavelet Transform domain and on the Discrete Fourier Transform domain and it makes use of feature extraction and selection techniques, as well as detection techniques supported on Bayes Theory and Multi-layer Perceptron Neural Networks. Comparative analysis show some advantages and disadvantages of each proposed technique and the relevance of them to solve the impulsive noise detection problem.

Matched frequency responses are a fundamental starting point for a variety ofimplementations for microphone arrays. In this report, two methods for frequencyresponse-calibration of a pre-assembled microphone array are presented andevaluated. This is done by extracting the deviation in frequency responses of themicrophones in relation to a selected reference microphone, using a swept sine asa stimulus signal and an inverse filter. The swept sine includes all frequencieswithin the bandwidth of human speech. This allows for a full frequency responsemeasurements from all microphones using a single recording.Using the swept sine, the deviation in frequency response between the microphonescan be obtained. This deviation represents the scaling factor that all microphonesmust be calibrated with to match the reference microphone. Applying the scalingfactors on the recorded stimulus signal shows an improvement for both implementedmethods, and where one method matches the frequency response of the microphoneswith high accuracy.Once the scaling factors of the various microphones is obtained, it can be usedto calibrate other recorded signals. This leads to an minor improvement formatching the frequency responses, as it has been shown that the differencesin frequency response between the microphones is signal-dependent and variesbetween recordings. The response differences between the microphones dependson the design of the array, speaker, room and the acoustic frequency dispersionthat occurs with sound waves. This makes it difficult to calibrate the frequencyresponses of the microphones without appropriate equipment because the responseof the microphones is noticeably affected by these other factors. Proposals to addressthese problems are discussed in the report as future work. / Matchade frekvenssvar är en grundläggande utgångspunkt för ett flertal implementationer för mikrofonarrayer. I denna rapport presenteras och utvärderas tvåmetoder för frekvenssvarskalibrering för en förmonterad mikrofonarray. Detta görsgenom att extrahera avvikelsen i frekvenssvar hos alla mikrofoner i förhållandetill en vald referensmikrofon. Frekvenssvaren tas fram med hjälp av ettsinussvep som stimulanssignal och ett inverterat filter. Sinussvepet inkluderar helafrekvensbredden för mänskligt tal och möjliggör att mikrofonernas fulla frekvenssvarkan analyseras från en enda inspelning.Med hjälp av sinussvepet kan avvikelsen i frekvenssvar mellan mikrofonerna erhållas.Denna avvikelse representerar den skalningsfaktor alla mikrofoner måste kalibrerasefter för att matcha referensmikrofonen. Genom att applicera faktorerna på deninspelade stimulussignalen ses en förbättring för båda implementerade metoderna,där en metod matchar mikrofonernas frekvenssvar med hög noggrannhet.När skalningsfaktorn för de olika mikrofonerna har erhållits kan den användas föratt kalibrera andra inspelade signaler. Detta leder till en liten förbättring i att matchafrekvenssvaren, då det har visat sig att skillnader mellan mikrofonernas frekvenssvarär signalberoende och varierar mellan inspelningar. Skillnader i frekvenssvar mellanmikrofonerna beror på ljudets utbredning i rummet, utformningen av arrayen,högtalaren och den akustiska frekvensspridningen som uppstår hos ljudvågor. Dettagör det svårt att kalibrera frekvenssvaren hos mikrofonerna utan lämplig utrustningeftersom mikrofonernas respons märkbart påverkas av dessa andra faktorer. Förslagför att kringgå dessa problem diskuteras i rapporten och tas upp som framtidaarbete.

Protein sequencing has produced overwhelming amount of protein sequences, especially in the last decade. Nevertheless, the majority of the proteins' functional and structural classes are still unknown, and experimental methods currently used to determine these properties are very expensive, laborious and time consuming. Therefore, automated computational methods are urgently required to accurately and reliably predict functional and structural classes of the proteins. Several bioinformatics methods have been developed to determine such properties of the proteins directly from their sequence information. Such methods that involve signal processing methods have recently become popular in the bioinformatics area and been investigated for the analysis of DNA and protein sequences and shown to be useful and generally help better characterise the sequences. However, there are various technical issues that need to be addressed in order to overcome problems associated with the signal processing methods for the analysis of the proteins sequences. Amino acid indices that are used to transform the protein sequences into signals have various applications and can represent diverse features of the protein sequences and amino acids. As the majority of indices have similar features, this project proposes a new set of computationally derived indices that better represent the original group of indices. A study is also carried out that resulted in finding a unique and universal set of best discriminating amino acid indices for the characterisation of allergenic proteins. This analysis extracts features directly from the protein sequences by using Discrete Fourier Transform (DFT) to build a classification model based on Support Vector Machines (SVM) for the allergenic proteins. The proposed predictive model yields a higher and more reliable accuracy than those of the existing methods. A new method is proposed for performing a multiple sequence alignment. For this method, DFT-based method is used to construct a new distance matrix in combination with multiple amino acid indices that were used to encode protein sequences into numerical sequences. Additionally, a new type of substitution matrix is proposed where the physicochemical similarities between any given amino acids is calculated. These similarities were calculated based on the 25 amino acids indices selected, where each one represents a unique biological protein feature. The proposed multiple sequence alignment method yields a better and more reliable alignment than the existing methods. In order to evaluate complex information that is generated as a result of DFT, Complex Informational Spectrum Analysis (CISA) is developed and presented. As the results show, when protein classes present similarities or differences according to the Common Frequency Peak (CFP) in specific amino acid indices, then it is probable that these classes are related to the protein feature that the specific amino acid represents. By using only the absolute spectrum in the analysis of protein sequences using the informational spectrum analysis is proven to be insufficient, as biologically related features can appear individually either in the real or the imaginary spectrum. This is successfully demonstrated over the analysis of influenza neuraminidase protein sequences. Upon identification of a new protein, it is important to single out amino acid responsible for the structural and functional classification of the protein, as well as the amino acids contributing to the protein's specific biological characterisation. In this work, a novel approach is presented to identify and quantify the relationship between individual amino acids and the protein. This is successfully demonstrated over the analysis of influenza neuraminidase protein sequences. Characterisation and identification problem of the Influenza A virus protein sequences is tackled through a Subgroup Discovery (SD) algorithm, which can provide ancillary knowledge to the experts. The main objective of the case study was to derive interpretable knowledge for the influenza A virus problem and to consequently better describe the relationships between subtypes of this virus. Finally, by using DFT-based sequence-driven features a Support Vector Machine (SVM)-based classification model was built and tested, that yields higher predictive accuracy than that of SD. The methods developed and presented in this study yield promising results and can be easily applied to proteomic fields.