Dissertações em Engenharia Elétrica (Mestrado) - PPGEE/ITEC

URI Permanente para esta coleçãohttps://repositorio.ufpa.br/handle/2011/2316

O Mestrado Acadêmico inicou-se em 1986 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 - 11 de 11

Acesso aberto (Open Access)
Algoritmo genético retroviral iterativo
(Universidade Federal do Pará, 2010-09-10) MOREIRA, Renato Simões; AFFONSO, Carolina de Mattos; http://lattes.cnpq.br/2228901515752720
This work presents the development of a hybrid meta-heuristic based on the viral life cycle, specifically from Retroviruses, which are part of nature’s swiftest forms. This algorithm is called Retroviral Iterative Genetic Algorithm (RIGA) and uses as computational basement Genetic Algorithm (GA) and biological basement retroviral replication characteristics, which provides a great diversity increasing the probability to find the solution, what is confirmed by better results obtained by AGRI than AG.
Acesso aberto (Open Access)
Alocação ótima de parques eólicos em sistemas de distribuição considerando incertezas de vento e carga utilizando algorítmo genético
(Universidade Federal do Pará, 2016-12-19) FONSECA, Sebastião Borges; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318; AFFONSO, Carolina de Mattos; http://lattes.cnpq.br/2228901515752720
This master’s thesis proposes a probabilistic approach to determine the optimal location, sizing and power factor of wind parks in power systems based on Genetic Algorithm. The proposed method considers the intermittent nature of wind and load to minimize the annual energy losses in the system under study. Recent technological advancement has created the opportunity to operate wind parks injecting reactive power into the system, making important to consider different power factor modes. The test system used is a distribution system with 33 buses and the results proved the efficiency of the proposed method and the importance to consider load and wind uncertainties in power system planning.
Acesso aberto (Open Access)
Análise estática e dinâmica de parques eólicos mistos compostos por aerogeradores de velocidade fixa e variável
(Universidade Federal do Pará, 2014-11-21) SILVA, Helleson Jorthan Brito da; AFFONSO, Carolina de Mattos; http://lattes.cnpq.br/2228901515752720
The wind electric power generation systems are presented as an appropriate solution to meet the technical, economic, environmental, social and governmental requirements, therefore they have been largely used in several countries. Among the wind turbines technologies available, the speed-fixed with squirrel cage induction generator and variable-speed with doubly fed induction generator wind systems are the most popular. With increasing of the wind penetration levels in electrical grid and due the technologic drawbacks of the wind turbine with cage induction generator, a tend to replace this concept by wind turbine with doubly fed induction generator in the construction of new wind farms is seen. In the case of wind plant installed already, for economic reasons, the gradative modernization process is more convenient. Although both wind systems are well known, but about the combined operation of them is known a little. Thus, the present paper proposes a study to evaluate the impacts occurred by integration of a mixed wind farm in the electrical grid, with squirrel cage and doubly fed induction generators. Aspects related to transient stability under fault and short wind variations and voltage stability are analyzed. The interactions between the technologies are also object of study. Cases with different penetration levels of the wind systems are considered, representing the gradative replace of part of the old concept wind turbine by others of the concept more modern. The results obtained show that the characteristics of the doubly fed induction generator allied with the reduction of the power injection of the fixed-speed wind systems improve the wind farm behavior, suggesting that the combined operation of the wind turbines could be a more costeffective solution.
Acesso aberto (Open Access)
Avaliação do impacto econômico da conexão de veículos elétricos e da geração eólica em redes inteligentes de energia
(Universidade Federal do Pará, 2018-02-26) VIEGAS, Marcel Augusto Alvarenga; NUNES, Marcus Vinicius Alves; http://lattes.cnpq.br/9533143193581447; AFFONSO, Carolina de Mattos; http://lattes.cnpq.br/2228901515752720
This master dissertation presents a power management tool of a power system operating in a Smart Grid that contains Electric Vehicles inserted as loads and Wind Power Generation. The optimization technique used was Simulated Annealing, in order to minimize the total energy cost of the network being studied. Three charging strategies were adopted: Peak Charging, Off-peak Charging and Smart Charging besides Demand Side Management techniques. In addition to the charging process will also be studied the Discharging of the battery electric vehicles, preferably at the peak of the load curve, as well as the possibility of supplying grid power through the wind farm to supply both loads in the topology of the system as for the loads of electric vehicles inserted through the creation of a charging/ discharging station. The system used is the IEEE - 39 bus New England power system. The results proved the effectiveness of the proposed method and the importance of considering besides distribution as well as generation and transmission in studies of planning, expansion, and operation of Smart Grids that contain Electric Vehicles and Wind Power Generation.
Acesso aberto (Open Access)
Avaliação dos impactos da recarga de veículos elétricos na vida útil de transformadores de distribuição
(Universidade Federal do Pará, 2023-10-30) BARROS, Amanda Monteiro Pinto; AFFONSO, Carolina de Mattos; http://lattes.cnpq.br/2580696185627481
The objective of this dissertation is to provide a comparative assessment of the impacts caused by charging practices of short-range and long-range electric vehicles under different power levels on life expectancy of distribution transformers. This research is based on the use of real data of residential energy consumption collected from the region of East Midlands, United Kingdom, as well as electric vehicle charging data collected through an experimental project also conducted in the United Kingdom. This study examines transformer hottest-spot temperature and evaluates the transformer accelerated aging factor that influences the equipment's lifespan according to the thermal model presented in IEEE Standard C57.91. As a result, this study reveals that the effects caused by long-range vehicles are more pronounced, as they charge at higher power level and the charging process is longer. As the penetration level of electric vehicles increases, transformer load and hottest-spot temperature increases, especially during winter season, where residential demand escalates. In the case of vehicles with 75 kWh, penetration levels starting from 30% already causes severe violations on transformer hottest-spot temperature, contributing to a reduction in the equipment's lifespan.
Acesso aberto (Open Access)
Estimação da porcentagem de flúor em alumina fluoretada proveniente de uma planta de tratamento de gases por meio de um sensor virtual neural
(Universidade Federal do Pará, 2011-06-22) SOUZA, Alan Marcel Fernandes de; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318; AFFONSO, Carolina de Mattos; http://lattes.cnpq.br/2228901515752720
The industries have been often seeking to reduce operating expenses, as to increase profits and competitiveness. To achieve this goal, it must take into account, among other factors, the design and implementation of new tools that accurately, efficiently and inexpensively allow access to information relevant to process. Soft sensors have been increasingly applied in industry. Since it offers flexibility, it can be adapted to make estimations of any measurement, thus a reducing in operating costs without compromising the measurements, and in some cases even improve the quality of generated information. Since they are completely softwarebased, they are not subjected to physical damage as the real sensors, and are better adaptated to harsh environments with hard access. The success of this king of sensors is due to the use of computational intelligence techniques, which have been widely used in the modeling of several nonlinear complex processes. This work aims to estimate the quality of alumina fluoride from a Gas Treatment Center (GTC), which is the result of gaseous adsorption on alumina virgin, using a soft sensor. The model that emulates the behavior of a alumina quality sensor the plant was created using an artificial intelligence technique known as Artificial Neural Network. The motivations of this work are: perform virtual simulations without compromising the GTC and make accurate decisions based not only on the operator's experience, to diagnose potential problems before they can interfere with the quality of alumina fluoride; maintain the aluminum reduction pot control variables within normal limits, since the production from low quality alumina strongly affects the reaction of breaking the molecule that contains this metal. The benefits this project brings include: increasing the GTC efficiency, producing high quality fluoridated alumina and emitting fewer greenhouse gases into the atmosphere and increasing the pot lifespan.
Acesso aberto (Open Access)
Gerenciamento de energia residencial com geração fotovoltaica utilizando recozimento simulado
(Universidade Federal do Pará, 2016-09-16) VILAR, Diego Branches; AFFONSO, Carolina de Mattos; http://lattes.cnpq.br/2228901515752720
The evolution of turning traditional grid into smart grid has contributed the enhancement of penetration of distributed generation in electric systems. Furthermore, the greater integration of Information and Communication Technology (ICT) and Power Electric Systems (PES) has provided the commitment among costumers and demand side management (DSM), which has become increasingly more interested in the last years. Nevertheless, being such a debated topic since 1980, it only has been more defunded throughout the smart grid. Hence this point of view, the costumer must understand some strategies of demand side management in order to take some advantage of this program. Therefore, this master’s dissertation aims developing a Home Energy Management System (HEM), in order to assist the costumer in making up his decision of the load shifting, providing scheduling proposals for home energy consumption, in Demand Response (DR) in order to reduce consumer spending on the purchase of energy provided by the concessionaire and optimize the use of photovoltaic generation.
Acesso aberto (Open Access)
Gerenciamento ótimo de um sistema de armazenamento de energia utilizando recozimento simulado
(Universidade Federal do Pará, 2018-01-18) ANGELIM, Jorge Henrique Costa; AFFONSO, Carolina de Mattos; http://lattes.cnpq.br/2228901515752720
This work proposes a method of energy management of a university campus, with a photovoltaic solar generation source and a energy storage system, which are connected to the main grid. The developed method aims to minimize the purchase of energy from the external grid using the Simulated Annealing optimization method. It is considered in the simulations variable energy tariffs according to the period of the day applied by the local utility. The obtained results were satisfactory, since the applied management scheme managed to reduce energy consumption significantly throughout the day, taking advantage of the energy stored at cheaper tariff schedules and greater availability of solar generation at peak times, making a minimum daily cost while it reaches the constraints associated with the problem. The obtained results showed the applicability of the optimization method, through the determination of operating points of the battery bank that maximized the use of available resources
Acesso aberto (Open Access)
Previsão da irradiação solar utilizando método ensemble para seleção de atributos e algoritmos de aprendizado de máquina
(Universidade Federal do Pará, 2023-06-20) MEJIA, Edna Sofia Solano; AFFONSO, Carolina de Mattos; http://lattes.cnpq.br/2228901515752720
Accurate forecasting of solar irradiance is essential for effective management of power systems with significant photovoltaic generation. Machine learning algorithms, which leverage historical data and patterns to make predictions, play a crucial role in this task. One key aspect is the use of ensemble models that combine the predictions of multiple algorithms to improve forecast accuracy and reliability. In this study, ensemble models are utilized to enhance the forecasting performance by aggregating the predictions of different algorithms. Moreover, the paper proposes an ensemble feature selection method, which involves identifying the most relevant input parameters and their related past observations. This approach aims to optimize the input features used by the machine learning algorithms, ensuring that only the most pertinent information is considered for accurate solar irradiance forecasts. By leveraging the strengths of multiple algorithms and selecting the most informative features, the ensemble approach offers a robust framework for improving the accuracy of solar irradiance predictions. The performance of several machine learning algorithms, including ensemble models, is compared for solar irradiance forecasting on days with different weather patterns using endogenous and exogenous inputs. The algorithms considered are AdaBoost, SVR, RF, XGBT, CatBoost, VOA, and VOWA. The proposed ensemble feature selection relies on the RF, IM, and Relief algorithms. The forecast accuracy is evaluated based on several metrics using a real database of the city of Salvador, Brazil. Different weather forecasts are considered: 1 hour, 2 hours, 3 hours, 6 hours, 9 hours, and 12 hours in advance. Numerical results show that the proposed ensemble feature selection improves forecast accuracy, and that the VOWA model selected with the best-performing algorithms presents forecasts with higher accuracy than the other algorithms at different forecast time horizons. This research demonstrates the effectiveness of ensemble models and feature selection techniques in enhancing solar irradiance forecasting, providing valuable insights for efficient power system management.
Acesso aberto (Open Access)
Regulação de tensão e frequência em micro-redes ilhadas com veículos elétricos e geração distribuída utilizando otimização por enxame de partículas
(Universidade Federal do Pará, 2019-08-16) SILVA, Rodrigo Veiga da; AFFONSO, Carolina de Mattos; http://lattes.cnpq.br/2228901515752720
This workpresents afrequency and voltage control implementation for a droop-regulated islanded microgrid through Direct Backward Forward Sweep Load-Flow Method (DBFS) modeled as a particle swarm optimization (PSO) problem. The PSO –allied with the Vehicle-to-Grid (V2G) technology of electric vehicles–controls the microgrid droop features to improve its steady-state frequency and voltage. Thus, working as a secondary control action. Simulations are performed in a 33-bus distribution system with distributed generators and electric vehicles operating over a 24-hour period.Theproposal of secondary control, based on PSO, proved to be versatile and efficient in face of load and generation variations.
Acesso aberto (Open Access)
Utilização de um sistema de armazenamento térmico para aplicação de gerenciamento pelo lado da demanda em uma rede de distribuição universitária
(Universidade Federal do Pará, 2018-08-16) SOUZA, Zaire de Assis Ferreira; AFFONSO, Carolina de Mattos; http://lattes.cnpq.br/2228901515752720
The present dissertation proposes the optimal management of the refrigeration demand of a university distribution network to promote greater economy and energy efficiency using the thermal inertia of air-conditioned classrooms as storage element of thermal energy and photovoltaic generation. The proposed method considers the hourly variation of the ambient temperature influencing the variation of the internal temperature of the classrooms and the consumption of energy throughout the day, knowing that the consumer unit is charged in the green model. The analysis of the loads of the consumer unit leads to the conclusion that most of the energy consumed is destined to some kind of refrigeration load, especially to ambient air conditioners, thus, to consider the management of this type of loads in the context of intelligent networks is a coherent way of positively impacting on increasing energy efficiency. Demand side management is based on Genetic Algorithm and the results prove the effectiveness of the proposed method in balancing the energy management and promoting a considerable increase in the economy with the energy expenses.

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