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Submitted by Emanuel Varela Cardoso (emanuel.varela@ufcg.edu.br) on 2018-05-07T20:16:46Z No. of bitstreams: 1 ANTONIO ISAAC LUNA DE LACERDA – TESE (PPGEE) 2016.pdf: 20857733 bytes, checksum: 28767af2f770d3e0a8b3544e02207602 (MD5) / Made available in DSpace on 2018-05-07T20:16:46Z (GMT). No. of bitstreams: 1 ANTONIO ISAAC LUNA DE LACERDA – TESE (PPGEE) 2016.pdf: 20857733 bytes, checksum: 28767af2f770d3e0a8b3544e02207602 (MD5) Previous issue date: 2016-04-29 / A cont abilidade do equipamento de acionamento estático é extremamente importante do ponto de vista e ficiência energética. A detecção da falha é necessária para preservar o desempenho do conversor por um maior tempo possível. Este trabalho investiga a capacidade de tolerância a falhas do inversor e retifi cador ANPC (Active Neutral Point Clamped ) de três níveis modi ficado, quando suas chaves são submetidas a falhas de circuito aberto e curto circuito. Com o objetivo de melhorar o comportamento do conversor quando da falha de uma chave, foram introduzidos tiristores adicionais, um para cada chave do braço do inversor, e fusíveis em série com as chaves de grampeamento. São apresentados métodos para detecção e identi ficação de falhas juntamente com esquemas de reconfi gurações para trinta tipos de falhas. Resultados de simulação e experimentais corroboram os estudo teóricos de operação dos conversores. Os resultados de simulação são obtidos a partir do software PSIM, enquanto os resultados experimentais são obtidos a partir de uma plataforma de desenvolvimento experimental controlado pelo processador digital de sinais TMS320F28335. / The power electronics equipment reliability is a very important aspect from the energy e -ciency point of view. So, fault detection and its compensation, becomes extremely necessary for maintaining the process under fault condition near normal operation for a period of time as long as possible. This work investigates the fault-tolerant capacity of a modi ed three-level ANPC (Active Neutral Point Clamped) inverter and recti er when its switches are submitted to open and short-circuit failures. Additional thyristors, one for each inverter main switch, and fuses in series with the clamping switches have been introduced in order to improve the converter behavior when a switch fails. Fault detection and identi cation methods are presented together with con gured schemes for thirty types of failures. Simulation and experimental results are presented in order to con rm the validity of the proposed solutions, the simulation results are obtained from the software PSIM, whereas the experimental results are obtained from one experimental development platform controlled by a digital signal processor TMS320F28335.

Orientador: Jozué Vieira Filho / Banca: Anna Diva Plasencia Lotufo / Banca: Marco Aparecido Queiroz Duarte / Resumo: Este trabalho apresenta uma metodologia para detecção e classificação de faltas do tipo curto-circuito. Esta operação de diagnóstico tem como principal operador um banco de redes neurais artificiais (RNAs) do tipo ARTMAP Nebulosa. Tal ferramenta segue a principal filosofia das RNAs: o reconhecimento de padrões, através de um trei- namento supervisionado inicial, responsável por atualizar os parâmetros das redes con- comitantemente com os resultados desejados. Finalizada a fase de adaptação, as redes serão capazes de, após receber novos padrões, classificá-los, de forma a propiciar, ao operador, informações importantes quanto ao estado de transmissão de barras presentes em um determinado complexo elétrico. Aqui, tomou-se como modelo a simulação de um grande alimentador real, composto por mais de 800 barras. Aliados às RNAs, tam- bém foram aplicadas, no que se refere à análise de dados amostrados de corrente elétri- ca, duas importantes e flexíveis ferramentas matemáticas: a Transformada de Fourier e a Transformada Wavelet. .Observa-se, ao final do trabalho, que os resultados apresenta- dos são bastante encorajadores, o que possibilitaria a utilização do programa em um ambiente real / Abstract: This work presents a detection and classification of short-circuit faults metho- dology. The main operator of this diagnostic operation is a Fuzzy ARTMAP Artificial Neural Networks (ANN) bank. This tool follows the ANN's main core: pattern recogni- tion, through an initial supervised training stage, responsible for updating the parame- ters of the networks con-comitant with the desired results. Completed the adaptation phase, the network will be able to, after receiving new standards, classifying them in order to provide to the operator important information about the state of transmission of bars present in a given complex electric. Here, there is, like a model, a simulation of a real big feeder, comprising more than 800 bars. Besides the ANNs, two important and flexible mathematical tools were also applied with regard to the analysis of sampled data of electric current: Fourier Transform and Wavelet Transform. At the end of the work the results presented are very encouraging, which would allow the use of the pro- gram in a no simulations real environment / Mestre

Orientador: Carlos Roberto Minussi / Resumo: Esta pesquisa apresenta o desenvolvimento de uma ferramenta para a detecção e classificação de curtos-circuitos em sistemas de distribuição de energia elétrica, a qual é baseada no uso combinado da análise multirresolução e rede neural ARTMAP-fuzzy. A análise multirresolução permite a identificação de singularidades nas oscilografias e a rede da família ART garante ao sistema classificador a capacidade de aprendizado contínuo de novos padrões sem perder o conhecimento previamente adquirido. Todo o processo de diagnóstico é realizado em uma única etapa, reduzindo o custo computacional da metodologia. A eficiência do sistema é verificada por uma análise direta, na qual se contabiliza a quantidade total de acertos, e por uma avaliação comparativa, a qual envolve a substituição da rede ARTMAP-fuzzy pela rede ARTMAP-Euclideana. Resultados mostram que o sistema é eficiente, sendo capaz de detectar e classificar 100% das falhas elétricas. / Abstract: This research presents the development of a tool for the detection and classification of short circuits in electric power distribution systems, which is based on the combined use of multi-resolution analysis and fuzzy ARTMAP neural network. The multiresolution analysis allows the identification of singularities in the oscillographs and the ART family network guarantees to the classifier the ability to continuously learn new patterns without losing the previously acquired knowledge. The entire diagnostic process is performed in a single step, reducing the computational effort. The efficiency of the system is verified by a direct analysis, in which the total number of hits is counted, and by a comparative evaluation, which involves the replacement of the fuzzy ARTMAP network by the Euclidean ARTMAP network. Results show that the system is efficient, being able to detect and classify 100% of the electrical faults. / Mestre

Made available in DSpace on 2014-06-11T19:22:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-02-19Bitstream added on 2014-06-13T20:09:42Z : No. of bitstreams: 1 martins_jrd_me_ilha.pdf: 2522090 bytes, checksum: f59e117ff382b22406c99cdc0096ef37 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Este trabalho apresenta uma metodologia para detecção e classificação de faltas do tipo curto-circuito. Esta operação de diagnóstico tem como principal operador um banco de redes neurais artificiais (RNAs) do tipo ARTMAP Nebulosa. Tal ferramenta segue a principal filosofia das RNAs: o reconhecimento de padrões, através de um trei- namento supervisionado inicial, responsável por atualizar os parâmetros das redes con- comitantemente com os resultados desejados. Finalizada a fase de adaptação, as redes serão capazes de, após receber novos padrões, classificá-los, de forma a propiciar, ao operador, informações importantes quanto ao estado de transmissão de barras presentes em um determinado complexo elétrico. Aqui, tomou-se como modelo a simulação de um grande alimentador real, composto por mais de 800 barras. Aliados às RNAs, tam- bém foram aplicadas, no que se refere à análise de dados amostrados de corrente elétri- ca, duas importantes e flexíveis ferramentas matemáticas: a Transformada de Fourier e a Transformada Wavelet. .Observa-se, ao final do trabalho, que os resultados apresenta- dos são bastante encorajadores, o que possibilitaria a utilização do programa em um ambiente real / This work presents a detection and classification of short-circuit faults metho- dology. The main operator of this diagnostic operation is a Fuzzy ARTMAP Artificial Neural Networks (ANN) bank. This tool follows the ANN’s main core: pattern recogni- tion, through an initial supervised training stage, responsible for updating the parame- ters of the networks con-comitant with the desired results. Completed the adaptation phase, the network will be able to, after receiving new standards, classifying them in order to provide to the operator important information about the state of transmission of bars present in a given complex electric. Here, there is, like a model, a simulation of a real big feeder, comprising more than 800 bars. Besides the ANNs, two important and flexible mathematical tools were also applied with regard to the analysis of sampled data of electric current: Fourier Transform and Wavelet Transform. At the end of the work the results presented are very encouraging, which would allow the use of the pro- gram in a no simulations real environment

Les convertisseurs DC-DC suscitent un intérêt considérable en raison de leur puissance élevée et de leurs bonnes performances. Ils sont particulièrement utiles dans les systèmes multisources de production d'énergie électrique. Toutefois, en raison du grand nombre de composants sensibles utilisés dans ces circuits et comprenant des semi-conducteurs de puissance, des bobines et des condensateurs, une probabilité non négligeable de défaillance des composants doit être prise en compte. Cette thèse considère l'un des convertisseurs DC-DC les plus prometteurs - le convertisseur ZVS à pont isolé de type Buck. Une approche en deux étapes est présentée pour détecter et isoler les défauts en circuit ouvert dans les semi-conducteurs de puissance des convertisseurs DC-DC. La première étape concerne la détection et la localisation des défauts dans un convertisseur donne. La seconde étape concerne sur les systèmes munis de plusieurs convertisseurs DC-DC. Les méthodes proposées sont basées sur les réseaux Bayesiens (BBN). Les signaux utilisés dans ces méthodes sont ceux des entrées de mesure du système de commande et aucune mesure supplémentaire n'est requise. Un convertisseur expérimental ZVS à pont isolé de type Buck a été conçu et construit pour valider la détection et la localisation des défauts Sur un seul convertisseur. Ces méthodes peuvent être étendues à d'autres types de convertisseurs DC-DC. / DC-DC converters have received significant interest recently as a result of their high power capabilities and good power quality. They are of particular interest in systems with multiple sources of energy. However due to the large number of sensitive components including power semiconductor devices, coils, and capacitors used in such circuits there is a high likelihood of component failure. This thesis considers one of the most promising DC-DC converters—the ZVS full bridge isolated Buck converter. An approach with two stages is presented to detect and isolate opencircuit faults in the power semiconductor devices in systems with DC-DC converters. The first stage is the fault detection and isolation for a single DC-DC converter, while the second stage works on a system with multiple DC-DC converters. The proposed methods are based on Bayesian Belief Network (BBN). The signals used in the proposed methods are already available as measurement inputs to control system and no additional measurements are required. An experimental ZVS full bridge isolated Buck converter has been designed and built to validate the fault detection and isolation method on a single converter. The methods can be used with other DC-DC converter typologies employing similar analysis and principals.

Mixed-Criticality Systems (MCSs) include tasks with multiple levels of criticality and different modes of operation. These systems bring benefits such as energy and resource saving while ensuring safe operation. However, management of available resources in order to achieve high utilization, low power consumption, and required reliability level is challenging in MCSs. In many cases, there is a trade-off between these goals. For instance, although using fault-tolerance techniques, such as replication, leads to improving the timing reliability, it increases power consumption and can threaten life-time reliability. In this work, we introduce an approach named Life−timePeakPower management inMixed−Criticalitysystems (LPP-MC) to guarantee reliability, along with peak power reduction. This approach maps the tasks using a novel metric called Reliability-Power Metric (RPM). The LPP-MC approach uses this metric to balance the power consumption of different processor cores and to improve the life-time of a chip. Moreover, to guarantee the timing reliability of MCSs, a fault-tolerance technique, called task re-execution, is utilized in this approach. We evaluate the proposed approach by a real avionics task set, and various synthetic task sets. The experimental results show that the proposed approach mitigates the aging rate and reduces peak power by up to 20.6% and 17.6%, respectively, compared to state-of-the-art.