Teses em Engenharia Elétrica (Doutorado) - PPGEE/ITEC

URI Permanente para esta coleçãohttps://repositorio.ufpa.br/handle/2011/2317

O Doutorado Acadêmico inicio-se em 1998 e pertence ao Programa de Pós-Graduação em Engenharia Elétrica (PPGEE) do Instituto de Tecnologia (ITEC) da Universidade Federal do Pará (UFPA).

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Agora exibindo 1 - 5 de 5

Acesso aberto (Open Access)
Algoritmos culturais com abordagem memética e multipopulacional aplicados a problemas de otimização
(Universidade Federal do Pará, 2012-04-20) SILVA, Deam James Azevedo da; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318
In many optimization problems is hard to reach a good result or a result close to the optimum value in a feasible time, especially when working on large scale. So, many of these problems are addressed by heuristics or metaheuristics running search for better solutions within the defined search space. Within the natural computing algorithms there are the cultural and genetic algorithms. These are evolutionary metaheuristics complement each other due to the dual mechanism of cultural heritage/genetic. The purpose of this paper is to study and use such mechanisms adding local search heuristics and multipopulation applied to combinatorial optimization problems (knapsack and travel salesman problems), constrained problems and multimodal functions. Some experiments have been conducted to assess the performance of the proposed combination of meta-heuristisc and heuristics mechanisms against approaches found in literature as applied to problem addressed here.
Acesso aberto (Open Access)
Estratégia de otimização para a melhoria da interpretabilidade de redes bayesianas: aplicações em sistemas elétricos de potência
(Universidade Federal do Pará, 2009-12-10) ROCHA, Cláudio Alex Jorge da; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
The study of methods, techniques and tools that can aid the decision processes in power systems, in its many sections, is a subject of great interest. This decision support can be accomplished through many different techniques, particularly those based on computational intelligence, given their applicability on domains with uncertainty. In this proposal, Bayesian networks are used for the extraction of knowledge models from the available data on power systems. Moreover, given the demands of these systems and some limitations imposed to the inferences in Bayesian networks, a method is proposed, using genetic algorithms, capable of extending the power of comprehensibility of the patterns discovered; it aims at finding the optimal scenario in order to attain a given target, considering the incorporation of a priori knowledge from domain specialists, identifying the most influent variables in the domain for the maximization of the target variable.
Acesso aberto (Open Access)
Experimentos de mineração de dados aplicados a sistemas scada de usinas hidrelétricas
(Universidade Federal do Pará, 2012-04-13) OHANA, Ivaldo; BEZERRA, Ubiratan Holanda; http://lattes.cnpq.br/6542769654042813
The current model of the Brazilian electric sector allows equal terms to all actors and reduces the role of the State in this sector. This model forces the electrical utilities to improve the quality of their products and, as a prerequisite for this purpose, they should make more effective use of the enormous amount of operational data that are stored in databases, acquired from the operation of their electrical systems which use the hydroelectric power plants as their main source of energy generation. One of the main tools for managing the operation of these plants are the Supervisory Control and Data Acquisition systems (SCADA). Thus, the large amount of data stored in databases by SCADA systems, certainly containing relevant information, should be treated to discover relationships and patterns that would help in the understanding of many important operational aspects as well as in the evaluation of operational performance of the electric power systems. The process of Knowledge Discovery in Database (KDD) is the process of identification of patterns in large data sets, that are valid, new, and useful to improve the understanding of a problem or a decision-making procedure. Data Mining is the step within KDD that extracts useful information from large databases. In this scenario, the present study objective is to perform data mining experiments on data generated by power plants SCADA systems, to produce relevant information to assist in planning, operation, maintenance and security of hydro power plants and also contribute to the implementation of the culture of using data mining techniques applied to these plants.
Acesso aberto (Open Access)
Um framework para a previsão de cenários com o uso de sistemas híbridos neurogenéticos para compra e venda de energia elétrica no mercado futuro
(Universidade Federal do Pará, 2012-05-04) CONDE, Guilherme Augusto Barros; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
In the context of time series forecasting, is great the interest in studies of forecasting methods of time series that can identify existing structures and patterns in historical data, allowing generate the next patterns of the series. The proposal defended in this thesis is the development of a framework that uses the full potential of forecasting techniques (neural networks) with the optimization techniques (genetic algorithms) in a hybrid system that well enjoy the advantages of each of these techniques to the generation of future scenarios that can show, in aaddition to normal forecasts based on historical values, alternative pathways of the curves of time series analyzed.
Acesso aberto (Open Access)
Predição de qualidade de experiência para internet do futuro em arquiteturas heterogêneas de redes sem fio móveis
(Universidade Federal do Pará, 2013-12-06) FERREIRA JUNIOR, José Jailton Henrique; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
The Next Generation Networks are architectures that allow vertical handovers and the user-centric vision by the appropriate Quality of Experience (QoE) provisioning for multimedia applications. The discussion is much less protocol-oriented perspective and is more service-oriented perspective. This thesis presents an architecture for next-generation networks to provide wireless heterogeneous access and seamless vertical handover for multimedia applications. The proposal considers different wireless technologies and also adopts the standard IEEE 802.21 (Media Independent Handover – MIH) to assist in the integration and the mobility management of heterogeneous wireless networks. The technologies in architecture are: IEEE 802.11 (popular known as Wi-Fi), IEEE 802.16 (popular known as WiMAX) and LTE (popular known as Fourth Generation – 4G). The objective is to choose the best connection for user. The proposal architecture presents mechanisms to predict quality of experience that will be decisive parameter to do or do not the handover, the prediction will be using artificial intelligence neural networks, in addition to architecture also provides a mechanism for QoE discard packets to specific multimedia applications. The proposal is evaluated by simulation using the ns-2 (Network Simulator) and the performance results are presented through the QoS/QoE metrics and also visually by displaying the video frames transmitted in architecture.

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