Light-emitting diodes (LEDs) offer energy-efficient lighting and are widely adopted. However, LED drivers that regulate power can fail when subjected to voltage disturbances on the electrical grid. This research investigates how components within LED drivers durability when undergoing voltage impulses and swells using simulation-based methods. An LED driver circuit was modeled in LTspice circuit simulation software. Impulses from 35-65V and equivalent voltage swells were applied to the simulated driver. The electrical stresses on components were statistically analyzed using the design of experiments and general full factorial. This methodology identified the most vulnerable components and their common durability/failure mechanisms during impulse and swell events. The findings provide insights into design changes that harden drivers against grid disturbances. This study determines that higher capacitor voltage ratings improved voltage impulse and swell withstand. Additionally, adding a surge suppression diode across the LED minimized diode reverse breakdown during swells. This simulation-based approach enables the informed design of robust LED drivers that can withstand electrical grid perturbations through strategic hardening of the most vulnerable components. The methodology and findings provide a framework for the reliability optimization of LED drivers and other power electronic systems exposed to power quality disturbances.Keywords: LED driver, voltage disturbance, component failure, circuit simulation, design of experiments (DOE), general full factorial.

Made available in DSpace on 2014-06-11T19:30:50Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-04-26Bitstream added on 2014-06-13T19:40:17Z : No. of bitstreams: 1 malange_fcv_dr_ilha.pdf: 2238559 bytes, checksum: 4603e9cf1612e9f68b0c3cf1e7a80e43 (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Muitos esforços têm sido despendidos para tentar sanar problemas relacionados com Qualidade da Energia Elétrica (QEE), principalmente na automação de processos e desenvolvimento de equipamentos de monitorização que possibilitem maior desempenho e confiabilidade a todo o Sistema Elétrico. Esta pesquisa apresenta um sistema eficiente de identificador/classificador automático de distúrbios chamado de Rede Neuro-Fuzzy-Wavelet. A estrutura básica dessa rede é composta por três módulos: o módulo de detecção de anomalias onde os sinais com distúrbios são identificados, o módulo de extração de características onde as formas de onda com distúrbio são analisadas, e o módulo de classificação que conta com uma rede neural ARTMAP Fuzzy, a qual indica qual o tipo de distúrbio sofrido pelo sinal. Os tipos de distúrbios incluem os isolados de curto prazo, tais como: afundamento de tensão (sag), elevação de tensão (swell), os distúrbios de longo prazo como distorção harmônica, bem como distúrbios múltiplos simultâneos como afundamento de tensão com distorção harmônica e elevação de tensão com distorção harmônica. A concepção do sistema de inferência (neural wavelet ARTMAP fuzzy) permite realizar a classificação dos referidos distúrbios de forma robusta e com grande rapidez na obtenção das soluções. Testes apontam para o alto desempenho dessa rede na detecção e classificação correta dos tipos de distúrbios de tensão analisados, 100% de acerto. A forma robusta e grande rapidez na obtenção dos resultados, possibilita sua aplicação em tempo real, visto que o esforço computacional, muito pequeno, é alocado, basicamente, na fase de treinamento. Somente uma pequena parcela de tempo computacional é necessária para a efetivação das análises. Além do mais, a metodologia proposta pode ser estendida para a realização de tarefas mais complexas... / Many efforts have been spent to solve problems related to Power Quality (PQ), principally in process automation and developing monitoring equipments that can provide more reliability and behavior for the electrical system. This research presents an efficient automatic system to identify/classify disturbs by Fuzzy Wavelet Neural Network. The basic structure of this neural network is composed of three modules such as: module for detecting anomalies where the signals with disturbs are identified, module for extracting the characteristics where the wave forms with disturbs are analyzed, and the module of classification that contains a fuzzy ARTMAP neural network that shows the type of disturbs existing in the signal. The types of disturbs include the short term isolated ones which are: voltage dip (sag), voltage increasing (swell); the long term disturbs such as harmonic distortion as well as the multiple simultaneous ones like the voltage dip with harmonic distortion and voltage increasing with harmonic distortion. The inference system (neural wavelet ARTMAP fuzzy) allows executing the classification of the cited disturbs very fast and obtaining reliable results. This neural network provides high performance when classifying and detecting the voltage disturbs very fast with about 100% of accuracy. The speed in obtaining the results allows an application in real time due to a low computational effort, which is basically in the training phase of the neural network. A little time of the computational effort is spent for the analysis. Moreover the proposed methodology can be used for realizing more complex tasks, as for example the localization of the power sources of the voltage disturbs. It is a very important contribution in the power quality, mainly to be a needy activity for solutions on the specialized literature

Orientador: Carlos Roberto Minussi / Banca: Anna Diva Plasencia Lotufo / Banca: Mara Lúcia Martins Lopes / Banca: Arlan Luiz Bettiol / Banca: Edmárcio Antonio Belati / Resumo: Muitos esforços têm sido despendidos para tentar sanar problemas relacionados com Qualidade da Energia Elétrica (QEE), principalmente na automação de processos e desenvolvimento de equipamentos de monitorização que possibilitem maior desempenho e confiabilidade a todo o Sistema Elétrico. Esta pesquisa apresenta um sistema eficiente de identificador/classificador automático de distúrbios chamado de Rede Neuro-Fuzzy-Wavelet. A estrutura básica dessa rede é composta por três módulos: o módulo de detecção de anomalias onde os sinais com distúrbios são identificados, o módulo de extração de características onde as formas de onda com distúrbio são analisadas, e o módulo de classificação que conta com uma rede neural ARTMAP Fuzzy, a qual indica qual o tipo de distúrbio sofrido pelo sinal. Os tipos de distúrbios incluem os isolados de curto prazo, tais como: afundamento de tensão (sag), elevação de tensão (swell), os distúrbios de longo prazo como distorção harmônica, bem como distúrbios múltiplos simultâneos como afundamento de tensão com distorção harmônica e elevação de tensão com distorção harmônica. A concepção do sistema de inferência (neural wavelet ARTMAP fuzzy) permite realizar a classificação dos referidos distúrbios de forma robusta e com grande rapidez na obtenção das soluções. Testes apontam para o alto desempenho dessa rede na detecção e classificação correta dos tipos de distúrbios de tensão analisados, 100% de acerto. A forma robusta e grande rapidez na obtenção dos resultados, possibilita sua aplicação em tempo real, visto que o esforço computacional, muito pequeno, é alocado, basicamente, na fase de treinamento. Somente uma pequena parcela de tempo computacional é necessária para a efetivação das análises. Além do mais, a metodologia proposta pode ser estendida para a realização de tarefas mais complexas... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Many efforts have been spent to solve problems related to Power Quality (PQ), principally in process automation and developing monitoring equipments that can provide more reliability and behavior for the electrical system. This research presents an efficient automatic system to identify/classify disturbs by Fuzzy Wavelet Neural Network. The basic structure of this neural network is composed of three modules such as: module for detecting anomalies where the signals with disturbs are identified, module for extracting the characteristics where the wave forms with disturbs are analyzed, and the module of classification that contains a fuzzy ARTMAP neural network that shows the type of disturbs existing in the signal. The types of disturbs include the short term isolated ones which are: voltage dip (sag), voltage increasing (swell); the long term disturbs such as harmonic distortion as well as the multiple simultaneous ones like the voltage dip with harmonic distortion and voltage increasing with harmonic distortion. The inference system (neural wavelet ARTMAP fuzzy) allows executing the classification of the cited disturbs very fast and obtaining reliable results. This neural network provides high performance when classifying and detecting the voltage disturbs very fast with about 100% of accuracy. The speed in obtaining the results allows an application in real time due to a low computational effort, which is basically in the training phase of the neural network. A little time of the computational effort is spent for the analysis. Moreover the proposed methodology can be used for realizing more complex tasks, as for example the localization of the power sources of the voltage disturbs. It is a very important contribution in the power quality, mainly to be a needy activity for solutions on the specialized literature / Doutor

Analysis of the dead-time effect and other nonlinearities of the voltage source inverter was carried out in the introduction of the doctoral thesis. Three compensation strategies for vector controlled PMSM were proposed based on the analysis. The voltage disturbance observer with cost function of current errors is based on the model of PMSM, known machine parameters and easily measurable quantities. The second observer which estimates the dq- axes currents and the value of the voltage error with one parameter only is designed based on the harmonic analysis and Kalman filter algorithm. The third method combines an adaptive approach with feedback and voltage disturbance observer that is based on the PMSM model. Furthermore, the two compensation methods for vector controlled induction motor were proposed. In the first case, the standard compensation strategy is extended by a harmonic compensator that suppresses the residual 6th harmonic component in dq- axes currents. The last strategy detects the polarity of the estimated phase currents that are obtained by the Kalman filter. All compensation strategies have been verified by MATLAB/Simulink simulations and by experiments on real drives.