Departamento de Engenharia Electromecânica
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Browsing Departamento de Engenharia Electromecânica by Field of Science and Technology (FOS) "Engenharia e Tecnologia::Engenharia Eletrotécnica e de Computadores"
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- Control of modular multilevel converters in high voltage direct current power systemsPublication . Mehrasa, Majid; Catalão, João Paulo da Silva; Calado, Maria do Rosário Alves; Pouresmaeil, EdrisThis thesis focuses on a comprehensive analysis of Modular Multilevel Converters (MMC) in High Voltage Direct Current (HVDC) applications from the viewpoint of presenting new mathematical dynamic models and designing novel control strategies. In the first step, two new mathematical dynamic models using differential flatness theory (DFT) and circulating currents components are introduced. Moreover, detailed step-by-step analysis-based relationships are achieved for accurate control of MMCs in both inverter and rectifier operating modes. After presenting these new mathematical equations-based descriptions of MMCs, suitable control techniques are designed in the next step. Because of the nonlinearity features of MMCs, two nonlinear control strategies based on direct Lyapunov method (DLM) and passivity theory-based controller combined with sliding mode surface are designed by the use of circulating currents componentsbased dynamic model to provide a stable operation of MMCs in HVDC applications under various operating conditions. The negative effects of the input disturbance, model errors and system uncertainties are suppressed by defining a Lyapunov control function to reach the integralproportional terms of the flat output errors that should be finally added to the initial inputs. Simulation results in MATLAB/SIMULINK environment verify the positive effects of the proposed dynamic models and control strategies in all operating conditions of the MMCs in inverter mode, rectifier mode and HVDC applications.
- Estudo das Limitações Energéticas em Sensores Inteligentes sem Fios Compatíveis com a Norma IEEE1451Publication . Pereira, João Luís Dâmaso; Espírito Santo, António Eduardo Vitória doEste estudo de investigação científica e tecnológica, surgiu inicialmente com a integração do projeto INDTECH 4.0 - Novas tecnologias para fábrica inteligente. Projeto que tem como principal objetivo a conceção e desenvolvimento de tecnologias inovadoras no contexto da Indústria 4.0. Do projeto fazem parte um conjunto de empresas e universidades, destacando-se a PSA – Peugeot Citroen em Mangualde. Com a massificação de sensores nas IoT (Internet of Things) há a necessidade de normalizar sistemas de rede de sensores sem fios. Com este estudo provou-se que é possível utilizar uma norma, já existente, numa rede de sensores inteligente sem fios. Neste estudo houve a necessidade de criar ferramentas de desenvolvimento que permitam de uma forma simplificada desenvolver um sensor sem fios. Foi ainda estudada a forma de como se integrar esta norma numa rede de sensores sem fios com limitações energéticas. A temática da proposta do trabalho centra-se no estudo da disponibilidade energética em sensores inteligentes sem fios, sendo estes compatíveis com a norma IEEE1451. A relevância e necessidade deste estudo justifica-se pelo facto de a recolha de energia ser, cada vez mais, uma solução tecnologicamente viável capaz de fornecer a energia necessária ao funcionamento de sensores inteligentes sem fios. A obtenção de resultados requereu a construção de plataformas de hardware e software, inexistentes, que suportam o desenvolvimento de sensores e atuadores que respeitam a norma IEEE1451. Foi construída uma rede de sensores inteligentes sem fios, que utiliza a energia recolhida do ambiente para operar. O estudo de diferentes cenários de funcionamento por parte do sensor inteligentes sem fios, integrado na rede sem fios, permite definir critérios de funcionamento e estabelecer os valores a assumir, em conformidade com a disponibilidade energética no nodo sensor num dado momento. O resultado deste trabalho terá um forte impacto na família da norma IEEE1451, em particular, nos resultados do grupo de trabalho IEEEP21451.002. Nas ferramentas de desenvolvimento especialmente concebidas para a norma, o estudo energético de uma TIM sem fios e uma proposta de adição de uma nova TEDS de energia para facilitar a implementação de TIM sem fios e com restrições energéticas.
- Intelligent Smart Wrist Band-based Health Monitoring of Car DriversPublication . Baiense, João Pedro da Silva; Velez, Fernando José da Silva; Pires, Ivan Miguel SerranoRoad accidents are often related to drivers’ psychological state and are frequently overlooked and dismissed. This includes the drivers’ mental health, which can be negatively affected by conditions such as stress, fatigue, and sadness. Emotional disturbances can impair driving abilities, posing significant risks to drivers and road users. Public health initiatives must focus on integrating innovative solutions to minimize fatalities and injuries. The Internet of Medical Things (IoMT) is a field of research that focuses on developing cost-effective nonintrusive new methods for assessing vital signs in non-clinical settings, such as homes and vehicles. The increasing use of these applications underscores the potential to address healthrelated road risks. Given the pressing concerns of road safety, this dissertation proposes the creation of an IoMT system that can revolutionize the driving experience. By introducing real-time monitoring of driver health, this system aims to address these challenges and significantly enhance road safety, a crucial need in our society. A systematic review, including the choice of thirtytwo relevant scientific publications on wearable devices for healthcare monitoring, was conducted to create a reliable system. The review utilized Natural Language Processing and the PRISMA methodology to analyze papers from various databases and considered population, methods, sensors, features, and communication protocols. The studies highlighted various hardware and software technologies used to enhance healthcare monitoring applications and the benefits and challenges associated with these applications, providing an overview of how to build an efficient system. Based on the results of the systematic review, the Driver Health System was proposed, integrating multiple layers with distinct roles to ensure efficiency and high performance. This dissertation proposes an innovative device for measuring the driver’s health data, integrating a comprehensive set of sensors and power management components to ensure reliable functionality. The device’s printed body encapsulates the PCB and battery, optimizing functionality and user comfort. The firmware developed for the device presented in this dissertation showcases the sensor drivers for photoplethysmography (PPG), accelerometer, barometric pressure, and fuel gauge sensors. The dissertation proposes a deep learning model designed to estimate the user’s heart rate by leveraging data from the PPG and accelerometer sensors. The model development involves multiple processing steps. Leaveone-session-out cross-validation and hyperparameter tuning techniques were employed for the model training and evaluation. The model achieved an outstanding Mean Absolute Error (MAE) of 3.450 ± 1.324 bpm and a Mean Squared Error (MSE) of 69.50 ± 93.57 bpm2 . The model was deployed in a custom WEB application for testing purposes. The dissertation describes the development of a custom mobile application for the Driver Health System, which offers crucial features such as intuitive real-time access to health status, device compatibility, power management, and integration of the heart rate estimation model to provide users with deeper insights into their health condition. This dissertation successfully enables a robust, innovative, real-time driver health monitoring solution. The Driver Health System represents a significant advancement at the intersection of healthcare industry and automotive sector. It aims to enhance road safety and establish a connected network that empowers to monitor and manage the drivers’ health effectively.
- Investigation on electricity market designs enabling demand response and wind generationPublication . Hajibandeh, Neda; Catalão, João Paulo da Silva; Mariano, Sílvio José Pinto Simões; Shafie-khah, MiadrezaDemand Response (DR) comprises some reactions taken by the end-use customers to decrease or shift the electricity consumption in response to a change in the price of electricity or a specified incentive payment over time. Wind energy is one of the renewable energies which has been increasingly used throughout the world. The intermittency and volatility of renewable energies, wind energy in particular, pose several challenges to Independent System Operators (ISOs), paving the way to an increasing interest on Demand Response Programs (DRPs) to cope with those challenges. Hence, this thesis addresses various electricity market designs enabling DR and Renewable Energy Systems (RESs) simultaneously. Various types of DRPs are developed in this thesis in a market environment, including Incentive-Based DR Programs (IBDRPs), Time-Based Rate DR Programs (TBRDRPs) and combinational DR programs on wind power integration. The uncertainties of wind power generation are considered through a two-stage Stochastic Programming (SP) model. DRPs are prioritized according to the ISO’s economic, technical, and environmental needs by means of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The impacts of DRPs on price elasticity and customer benefit function are addressed, including the sensitivities of both DR parameters and wind power scenarios. Finally, a two-stage stochastic model is applied to solve the problem in a mixed-integer linear programming (MILP) approach. The proposed model is applied to a modified IEEE test system to demonstrate the effect of DR in the reduction of operation cost.
- Optimização de recursos hídricos em coordenação com a produção eólicaPublication . Silva, António José Cerejo da; Mariano, Sílvio José Pinto SimõesA produção diária de energia eólica é incerta. Eventuais desequilíbrios entre energia previamente acordada fornecer e energia efectivamente fornecida ao mercado do dia seguinte conduzem a aumentos dos custos do sistema que deverão ser remunerados pelos produtores em incumprimento, diminuindo assim os seus lucros, ou inviabilizando mesmo a sua participação no mercado de energia eléctrica. Uma forma de evitar custos decorrentes de incumprimento por parte dos produtores eólicos consiste na realização de lances de mercado em conjunto com produtores hídricos. Este trabalho propõe uma metodologia operacional para produção hidro-eólica que visa a optimização de lances conjuntos no mercado do dia seguinte. O problema de optimização é formulado na tese como um problema de optimização de produção hídrica em que são conhecidas, uma previsão de produção eólica, e uma previsão dos preços de energia no mercado pool do dia seguinte. O objectivo estabelecido consiste em satisfazer o lance de mercado acordado, anulando eventuais incumprimentos de energia por parte da produção eólica em ambiente de incerteza. O problema é resolvido em duas instâncias distintas: (i) uma em que a optimização hídro-eólica não tem recurso de bombagem e (ii) outra em que a optimização hidro-eólica tem recurso de bombagem, avaliando os benefícios operacionais decorrentes da licitação conjunta. Os erros na previsão da potência eólica têm um efeito sobre a solução óptima para os lances conjuntos que não é simétrico: aos erros por defeito corresponde em geral uma depreciação maior da solução óptima que a valorização correspondente aos erros por excesso. Por isso, e de forma a não prejudicar a futura eficiência da operação hídrica, os lances conjuntos devem procurar corrigir essa assimetria, sendo mais conservadores sobre a previsão da produção eólica. E devem ser tanto mais conservadores quanto maior for a incerteza sobre essas previsões. Na tese é quantificada a redução sobre a previsão da produção eólica necessária para neutralizar o efeito desta assimetria, como função da incerteza da previsão.
- Optimization of 5G Second Phase Heterogeneous Radio Access Networks with Small CellsPublication . Khan, Bahram; Velez, Fernando José da SilvaDue to the exponential increase in high data-demanding applications and their services per coverage area, it is becoming challenging for the existing cellular network to handle the massive sum of users with their demands. It is conceded to network operators that the current wireless network may not be capable to shelter future traffic demands. To overcome the challenges the operators are taking interest in efficiently deploying the heterogeneous network. Currently, 5G is in the commercialization phase. Network evolution with addition of small cells will develop the existing wireless network with its enriched capabilities and innovative features. Presently, the 5G global standardization has introduced the 5G New Radio (NR) under the 3rd Generation Partnership Project (3GPP). It can support a wide range of frequency bands (<6 GHz to 100 GHz). For different trends and verticals, 5G NR encounters, functional splitting and its cost evaluation are well-thought-out. The aspects of network slicing to the assessment of the business opportunities and allied standardization endeavours are illustrated. The study explores the carrier aggregation (Pico cellular) technique for 4G to bring high spectral efficiency with the support of small cell massification while benefiting from statistical multiplexing gain. One has been able to obtain values for the goodput considering CA in LTE-Sim (4G), of 40 Mbps for a cell radius of 500 m and of 29 Mbps for a cell radius of 50 m, which is 3 times higher than without CA scenario (2.6 GHz plus 3.5 GHz frequency bands). Heterogeneous networks have been under investigation for many years. Heterogeneous network can improve users service quality and resource utilization compared to homogeneous networks. Quality of service can be enhanced by putting the small cells (Femtocells or Picocells) inside the Microcells or Macrocells coverage area. Deploying indoor Femtocells for 5G inside the Macro cellular network can reduce the network cost. Some service providers have started their solutions for indoor users but there are still many challenges to be addressed. The 5G air-simulator is updated to deploy indoor Femto-cell with proposed assumptions with uniform distribution. For all the possible combinations of apartments side length and transmitter power, the maximum number of supported numbers surpassed the number of users by more than two times compared to papers mentioned in the literature. Within outdoor environments, this study also proposed small cells optimization by putting the Pico cells within a Macro cell to obtain low latency and high data rate with the statistical multiplexing gain of the associated users. Results are presented 5G NR functional split six and split seven, for three frequency bands (2.6 GHz, 3.5GHz and 5.62 GHz). Based on the analysis for shorter radius values, the best is to select the 2.6 GHz to achieve lower PLR and to support a higher number of users, with better goodput, and higher profit (for cell radius u to 400 m). In 4G, with CA, from the analysis of the economic trade-off with Picocell, the Enhanced multi-band scheduler EMBS provide higher revenue, compared to those without CA. It is clearly shown that the profit of CA is more than 4 times than in the without CA scenario. This means that the slight increase in the cost of CA gives back more than 4-time profit relatively to the ”without” CA scenario.
- Stochastic management framework of distribution network systems featuring large-scale variable renewable energy sources and flexibility optionsPublication . Cruz, Marco Rafael Meneses; Catalão, João Paulo da Silva; Mariano, Sílvio José Pinto Simões; Fitiwi, Desta ZahlayThe concerns surrounding climate change, energy supply security and the growing demand are forcing changes in the way distribution network systems are planned and operated, especially considering the need to accommodate large-scale integration of variable renewable energy sources (vRESs). An increased level of vRESs creates technical challenges in the system, bringing a huge concern for distribution system operators who are given the mandate to keep the integrity and stability of the system, as well as the quality of power delivered to end-users. Hence, existing electric energy systems need to go through an eminent transformation process so that current limitations are significantly alleviated or even avoided, leading to the so-called smart grids paradigm. For distribution networks, new and emerging flexibility options pertaining to the generation, demand and network sides need to be deployed for these systems to accommodate large quantities of variable energy sources, ensuring an optimal operation. Therefore, the management of different flexibility options needs to be carefully handled, minimizing the sideeffects such as increasing costs, worsening voltage profile and overall system performance. From this perspective, it is necessary to understand how a distribution network can be optimally operated when featuring large-scale vRESs. Because of the variability and uncertainty pertinent to these technologies, new methodologies and computational tools need to be developed to deal with the ensuing challenges. To this end, it is necessary to explore emerging and existing flexibility options that need to be deployed in distribution networks so that the uncertainty and variability of vRESs are effectively managed, leading to the real-time balancing of demand and supply. This thesis presents an extensive analysis of the main technologies that can provide flexibility to the electric energy systems. Their individual or collective contributions to the optimal operation of distribution systems featuring large-scale vRESs are thoroughly investigated. This is accomplished by taking into account the stochastic nature of intermittent power sources and other sources of uncertainty. In addition, this work encompasses a detailed operational analysis of distribution systems from the context of creating a sustainable energy future. The roles of different flexibility options are analyzed in such a way that a major percentage of load is met by variable RESs, while maintaining the reliability, stability and efficiency of the system. Therefore, new methodologies and computational tools are developed in a stochastic programming framework so as to model the inherent variability and uncertainty of wind and solar power generation. The developed models are of integer-mixed linear programming type, ensuring tractability and optimality.