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 - 13 de 13

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Aprendizagem profunda aplicada a telecomunicações: classificação de modulação e controle de congestionamento
(Universidade Federal do Pará, 2019-05-31) NASCIMENTO, Ingrid Ariel Silva; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284
The goal of this dissertation is to explore Deep Learning (DL) techniques applied to Telecommunications. DL has achieved success in areas such as computer vision and object detection and it is timely to investigate DL in communication problems. Then, two distinct problems are investigated. First, DL is applied to Automatic Modulation Classification (AMC) to detect the adopted modulation scheme automatically. AMC is important, for instance, in Cognitive Radios and military applications. In this dissertation, we discuss the benefits and drawbacks of using DL as an alternative for AMC and show its efficiency in comparison to other machine learning methods applied to AMC. Other DL application in communication is Congestion Control. The context is related to Fronthaul in C-RAN architecture using congestion control in order to attend the strict requirements of the 5G system. Specifically, DL is investigated in conjuction with Deep Reinforcement Learning (DRL) techniques. In this topic, this dissertation presents a framework for investigations in congestion control for Fronthaul, and the implementation of a model and environment using NS-3 and Gym API of the OpenAI group for simulation. The developed framework is validated with preliminary experiments that compare Deep Reinforcement Learning methods with traditional congestion control techniques, using as figu
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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.
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Compression of activation signals from partitioned deep neural networks exploring temporal correlation
(Universidade Federal do Pará, 2024-11-27) SILVA, Lucas Damasceno; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284
The use of artificial neural networks for object detection, along with advancements in 6G and IoT research, plays an important role in applications such as drone-based monitoring of structures, search and rescue operations, and deployment on hardware platforms like FPGAs. However, a key challenge in implementing these networks on such hardware is the need to economize computational resources. Despite substantial advances in computational capacity, implementing devices with ample resources remains challenging. As a solution, techniques for partitioning and compressing neural networks, as well as compressing activation signals (or feature maps), have been developed. This work proposes a system that partitions neural network models for object detection in videos, allocating part of the network to an end device and the remainder to a cloud server. The system also compresses the feature maps generated by the last layers on the end device by exploiting temporal correlation, enabling a predictive compression scheme. This approach allows neural networks to be embedded in low-power devices while respecting the computational limits of the device, the transmission rate constraints of the communication channel between the device and server, and the network’s accuracy requirements. Experiments conducted on pre-trained neural network models show that the proposed system can significantly reduce the amount of data to be stored or transmitted by leveraging temporal correlation, facilitating the deployment of these networks on devices with limited computational power
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Controle linear quadrático gaussiano de um quadricóptero baseado em um filtro de Kalman estendido com variável instrumental
(Universidade Federal do Pará, 2024-02-08) SODRÉ, Lucas de Carvalho; SILVEIRA, Antonio da Silva; http://lattes.cnpq.br/1828468407562753; https://orcid.org/0000-0002-2698-2677
Given the transformations and promotion of technologies and modernization in different areas of society, such as the use of Unmanned Aerial Vehicles performing numerous automated activities, it is necessary to create algorithms with efficiency and safety to avoid losses and damages in their functions. Aerial systems are, for the most part, multiple input and multiple output systems, time-varying, susceptible to disturbances and measurement noise, becoming a challenging scenario in the area of system identification. Given this, such dynamics must be considered in the identification process. Therefore, the objective of this work is to develop an algorithm capable of jointly estimating the states and parameters of systems, mitigating the interference of measurement noise and external disturbances in the real-time identification process. Based on these principles, the creation of the joint estimation algorithm Extended Kalman Filter with Instrumental Variables was established. The proposed algorithm stands out for its theoretical commitment to minimizing interference from dynamics that can affect the reliability of parameters calculated by identification methods already consolidated in the literature, such as Extended Kalman Filter (EKF) and Recursive Least Squares (RLS). The proposed method was tested to calculate the stochastic linear model of the autopilot system of the unmanned aerial quadcopter, Parrot’s AR Drone 2.0 model, taking into account scenarios in which the sensor signal presents a signal-to-noise ratio of 100, 50, 10. Its performance was compared with RLS and EKF parameter estimation. To evaluate the state estimates, the root-mean-square deviation norm index was used and, to evaluate the parameters, the Euclidean distance between the real parameters and the estimated parameters was used. Finally, the data collected by the methods were used to tune the Gaussian Quadratic Linear Control controller, thus allowing comparison of the impact of the identification method on the closed-loop behavior of the aerial system. To enable discussion and comparison of control algorithms, the Squared Error Integral and Squared Control Integral indices were applied to evaluate the control performance, the gain margin and the phase margin to measure system robustness.
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Controle MPC multivariável com restrições usando funções de Laguerre
(Universidade Federal do Pará, 2018-03-01) PINHEIRO, Tarcísio Carlos Farias; SILVEIRA, Antonio da Silva; http://lattes.cnpq.br/1828468407562753
This work presents a constrained multivariable model predictive controller using Laguerre Functions. This controller uses a set of orthonormal Laguerre networks for representation of the control trajectory within a control horizon. In order to demonstrate the advantages of applying this type of controller in MIMO (Multiple-Input and Multiple-Output) systems, the Laguerre Functions Functions are used to decrease the computational load used to calculate the optimal control. In addition, It improves the compromise between control signal viability and closed-loop performance of the system. The Laguerre Functions are also used in conjunction with Hildreth’s Quadratic Programming to find the optimal solution for the case where the control signal is constrained. The proposed controller presents advantages when compared to the classical model predictive control approach, where forward shift operators are used to predict the future trajectory of the control signal, leading to unsatisfactory solutions and a high computational load for cases where the control signal demands a long prediction horizon and a high closed-loop performance.It is also reported the practical testes with a robotic manipulator configured as a MIMO system with three inputs and three outputs and tests simulated with the Wood and Berry binary distillation column which is a MIMO system with two inputs and two outputs, also containing transport time delays. The tests aim to compare the controller results presented with the traditional predictive control approach and thereby demonstrate the advantages of the method using the Laguerre functions and their efficiency for MIMO systems.
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Estudo comparativo de técnicas de inteligência de enxame na redução da ordem de sistemas dinâmicos lineares
(Universidade Federal do Pará, 2019-12-17) SILVA, Marlon John Pinheiro; SILVA, Orlando Fonseca; http://lattes.cnpq.br/7387718587227127; COSTA JÚNIOR, Carlos Tavares da; http://lattes.cnpq.br/6328549183075122
A redução de ordem de modelos tem se mostrado um problema bastante recorrente e diversas técnicas surgiram ao longo dos anos, quando, do ponto de vista do projeto de controladores, se tornou inadequada a elaboração e construção destes, visto o alto grau de redundância, que sistemas físicos reais de grande porte podem possuir. No âmbito da matemática determinística, muitos trabalhos, já consagrados na literatura, se propuseram a resolver tal problemática. Recentemente, técnicas que envolvem métodos metaheurísticos em um espaço de busca pré-determinado, utilizando Inteligência de Enxames, vêm sendo utilizados com bastante êxito e tem se mostrado uma nova ferramenta como solução. Com base neste contexto, este trabalho apresenta a compreensão do problema sob o ponto de vista da teoria de sistemas lineares; realizando um estudo comparativo entre as Inteligências de Enxames: Firefly Algorithm, enxame de partículas (PSO do inglês - Particle Swarm Optimization) e SFLA (do inglês - Shuffled Frog Leaping Algorithm).
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Metodologia para estimação de intenção de movimento e controle em tempo real de prótese mioelétrica de mão: uma abordagem linear, preditiva e estocástica
(Universidade Federal do Pará, 2018-03-28) DUTRA, Bruno Gomes; SILVEIRA, Antonio da Silva; http://lattes.cnpq.br/1828468407562753
Muscle signals from electromyography (EMG) are widely used to detect muscle contraction and intention to motion. By using these signals in real time in prosthetic control, a low signal to noise ratio is commonly found. Thus, it is necessary to have recursive methods, robust to noise and efficient algorithms, to generate commands in real time for the robotic actuator. In this research, stochastic system indentification techniques, Kalman filter, sensor fusion and stochastic predictive control techniques were investigated and applied to improve the measurement and processing of electromyographic signals to increase robustness in the control of biomechatronic prostheses. Thus, it is an improved process, less sensitive to noise and with minimal delays and phase lags. In this methodology, a four-stage distribution method is used: (1) features extraction by using an autoregressive model (AR), (2) data fusion with the Kalman filter, (3) motion estimation algorithm, and (4) predictive control with the generalized minimum variance controller applied to a servomechanism. The main objectives were: to enhance the signal-to-noise ratio of EMG signals, to have a low-cost real-time processing man-machine interface, to avoid measurement problems and to minimize energy consumption of the control system. A didactic plant was developed, which is a 4 channel EMG data acquisition and processing system with a servomechanism and its control system coupled in a robotic jaw. Practical tests were conducted with the prototype and the results show that it is possible to continuously estimate the intention of opening and closing movement of the hand and can confirm the good performance of the stochastic controller designed for the control of the electric prosthesis.
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Modelagem e controle robusto paramétrico aplicados a sistemas driven-right-leg para rejeição de ruídos em amplificadores biopotenciais.
(Universidade Federal do Pará, 2022-03-30) GOMES, Alan Sovano; FONSECA, Maria da Conceição Pereira; http://lattes.cnpq.br/3496755183083633; BARRA JUNIOR, Walter; http://lattes.cnpq.br/0492699174212608
The Driven-Right-Leg (DRL) system is widely applied to mitigate the effects of common mode voltage in biopotential amplifiers. It works as a closed-loop controller, whose objective is to reject disturbances caused by the capacitive coupling of the human body with the power line. In this work, the DRL system is evaluated from a robust parametric control point of view, with the intention of doing a more complete evaluation than the one found in the literature, measuring gain, phase and module extremal margins. The range of interval parametric variations, found in the literature, were used to describe the parametric uncertainties that disturb the studied system. Furthermore, a Lead-Lag controller was designed based on the model under parametric variation obtained, showing how both the analysis and synthesis of DRL controllers can be done with the presented theory. The results obtained were discussed in comparison with the DRL systems found in the specialized literature.
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Performance evaluation of robust parametric control strategies applied on suppression of oscillations effects due to constant power loads in multi-converter buck-buck systems
(Universidade Federal do Pará, 2018-06-11) MARCILLO, Kevin Eduardo Lucas; BARRA JUNIOR, Walter; http://lattes.cnpq.br/0492699174212608
Multi-converter electronic systems are becoming widely used in many industrial applications; therefore, the stability of the cascaded system is a big concern to real-world power supplies applications. Instability in cascaded systems may occur due to the constant power load (CPL), which is a behavior of the tightly regulated converters. CPLs exhibit incremental negative resistance behavior causing a high risk of instability in interconnected converters; therefore, the mitigation of this problem is an important issue in the multiple-stage switched mode power supply design. Thus, it is important to guarantee stability of the whole system. However, some difficulties remains besides the CPL, e.g., non-linearities due to the inductive element and uncertainties due to imprecision of mathematical models and/or variation of nominal values of the discrete elements that compose the DC/DC buck converter. Aiming to evaluate the performance of the proposed robust methodologies in this work to mitigate the instability problem caused by a CPL, several tests were developed by using an experimental plant and Matlab/Simulink, when the multi-converter buck-buck system is subjected a variation of power reference. The results show the improved performance of the proposed methodologies.
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Previsão da demanda de energia elétrica utilizando lógica fuzzy e função de autocorrelação estendida- um estudo de caso aplicado ao Estado de Rondônia
(Universidade Federal do Pará, 2018-04-12) OLIVEIRA, Paulo de Tarso Carvalho de; MACEDO, Valquíria Gusmão; http://lattes.cnpq.br/4288739747304808; COSTA JÚNIOR, Carlos Tavares da; http://lattes.cnpq.br/6328549183075122
The study of demand forecast correlated to time series of electric energy develops an optimization process for the supply of electricity, with the objective of improving the forecasting routine. In this study, it is presented a comparison of three autoregressive models used for the mentioned optimization, with the fuzzy logic model, dimensioned through the extended autocorrelation function. The electric energy consumption data presents a seasonal time series structure, provoking a historical cut with the electric power consumption characteristics of the State of Rondônia, and in this object was implemented a methodology of prediction by means of already sedimented models and in comparison to a new model, presented in Fuzzy Identification Methods for Autoregressive Models Using the Extended Autocorrelation Function. The performance of the models and application for short-term electricity demand forecasting, 5 (five) five business days of the week, was analyzed for the purpose of contracting electric energy packages with the electricity distribution concessionaires, in compliance with the legislation current agreement on auctions and purchase agreements. In the course of the work, we analyzed the results of electric energy prediction, by the models presented, the proposed model in Fuzzy System related to Extended Autocorrelation, being the most satisfactory one confirmed by errors of forecast in relation to the demand of electric power for the State of Rondônia, and complying with legislation on forecasting and demand of electric energy in Brazil.
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Projeto de controlador baseado em inequações matriciais lineares aplicado a um sistema multiconversor sujeito à incertezas paramétricas
(Universidade Federal do Pará, 2024-08-23) SILVA JUNIOR, Carlos Roozenbergh Porto da; BARRA JUNIOR, Walter; http://lattes.cnpq.br/0492699174212608
Conversion systems are critically important devices in electrical systems across various environments, especially in modern times, as multiple components with different voltage levels and sources are interconnected within a single system. Consequently, dynamic study methods of this network are examined using approaches that simplify the network based on speed modes and switching of other conversion systems, wherein fast systems are simplified to constant power loads (CPL). This method evaluates the network’s stability conditions. The study reveals that CPLs act as negative incremental resistances, and when analyzed through a linear model, it is observed that such loads reduce system damping, thereby decreasing stability margins and potentially rendering the system unstable. Additionally, uncertainties in the physical components of the circuit further affect the stability and performance of microgrids. Hence, designing regulators to mitigate oscillations caused by these effects becomes crucial to ensure the proper performance of these systems.In this work, a robust controller is designed to handle uncertainties and attenuate oscillations in the presence of constant power loads. This controller is implemented in a microgrid composed of two cascaded DC-DC buck converters, one of which is modeled as a CPL. The system model is utilized for both stability analysis and robust controller design in state space, where the compensator synthesis is structured in the form of a linear matrix inequality, solved using system optimization tools. The controller’s results are compared with another controller based on pole placement in both linear and nonlinear switched models, within the Matlab/Simulink simulation platform. Transient response and control signals are evaluated graphically and through performance indices under various operating conditions, including load disturbances and system parameter variations.
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Sistema de automação IoT para gestão de ativos no cenário da indústria 4.0
(Universidade Federal do Pará, 2023-07-10) GOMES, Woldson Leonne Pereira; SERUFFO, Marcos César da Rocha; http://lattes.cnpq.br/3794198610723464; SILVEIRA, Antonio da Silva; http://lattes.cnpq.br/1828468407562753
The fourth industrial revolution has several pillars, with the Internet of Things and Big Data being one of the most prominent. These technologies make it possible to collect and analyze large data sets in real time, allowing the development of models for the most diverse situations, from consumer behavior to the prevention of manufacturing failures. In this context, the present work addresses the development of a complete architecture for the implementation of an automation system for Industry 4.0, at the hardware and software level, based on the collection of temperature, hour meter, vibration and current data in electric motors of a primary aluminum industry. From the measured variables, vibration data can be obtained in the frequency domain, phase imbalance and Kalman filter estimation. A multicriteria decision algorithm was adopted to assist in choosing the programming language. After elaborating this systematic, a set of solutions was obtained that made the development of the system feasible, which was validated in a controlled experimental setup. The automation system developed was called IOTCORE, which performs the collection of variables in real time, with low latency, high performance, and makes it possible to transmit, store and visualize the data in several supervisory systems.
Acesso aberto (Open Access)
Técnicas de controle robusto baseadas em resposta em frequência e via alocação de polos intervalar para sistemas com incertezas paramétricas aplicadas ao problema de regulação de tensão em conversores de potência.
(Universidade Federal do Pará, 2022-05-12) CARDOZO, Luise Ferreira; BARRA JUNIOR, Walter; http://lattes.cnpq.br/0492699174212608
Microgrids are a form of distribution system, which belong to the broad concept of smart grids. Multiconverter or multilevel systems are nothing more than DC microgrids composed of several power converters connected in cascade and/or in parallel. In this way, the multiconverter system described in this thesis has a DC - DC converter in the Buck topology, which is used as a source of direct voltage for the main bus of the microgrid, being an element of fundamental importance and whose voltage control is essential, because electronic loads are sensitive to voltage deviations. In order to control the voltage on the DC bus, the system is first modeled using the recursive least squares method, at which time the parametric variations are obtained forming a more comprehensive model called the interval transfer function, which is represented graphically by the extreme set. In a second moment, two robust controllers are developed, one through the extreme stability margins of the model culminating in a PI controller based on frequency response, and the other through an interval pole allocation control project in PID format. The robust performance of the controllers is evaluated through computational simulation, experimentally in the multiconverter system and, finally, using a quantitative analysis through performance indices.

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