Simulation of a-Si PV system grid connected by boost and inverter

This paper is about a PV system connected to the electric grid by power electronic converters, using classical PI controller. The modelling for the converters emulates the association of a DC-DC boost with a two-level power inverter (TwLI) or three-level power inverter (ThLI) in order to follow the performance of a testing experimental system. Pulse width modulation (PWMo) by sliding mode control (SMCo) associated with space vector modulation (SVMo) is applied to the boost and the inverter. The PV system is described by the five parameters equivalent circuit. Parameter identification and simulation studies are performed for comparison with the testing experimental system.

Low power low voltage quadrature RC oscillators for modern RF receivers

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

Energy evaporation: the new concept of indoor systems for WPT and EH embedded into the floor

This work introduces a novel idea for wireless energy transfer, proposing for the first time the unit-cell of an indoor localization and RF harvesting system embedded into the floor. The unit-cell is composed by a 5.8 GHz patch antenna surrounded by a 13.56 MHz coil. The coil locates a device and activate the patch which, connected to a power grid, radiates to wirelessly charge the localized device. The HF and RF circuits co-existence and functionality are demonstrated in this paper, the novelty of which is also in the adoption of low cost and most of all ecofriendly materials, such as wood and cork, as substrates for electronics.

Time-domain optimization of amplifiers based on distributed genetic algorithms

Thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Electrical and Computer Engineering

Simulation of a-Si PV system linked to the grid by DC boost and three-level inverter under cloud scope

This paper is about a PV system linked to the electric grid through power converters under cloud scope. The PV system is modeled by the five parameters equivalent circuit and a MPPT procedure is integrated into the modeling. The modeling for the converters models the association of a DC-DC boost with a three-level inverter. PI controllers are used with PWM by sliding mode control associated with space vector modulation controlling the booster and the inverter. A case study addresses a simulation to assess the performance of a PV system linked to the electric grid. Conclusions regarding the integration of the PV system into the electric grid are presented. © IFIP International Federation for Information Processing 2015.

Desenvolvimento de um Controlador de Boundary Scan (Ieee 1149.1)

Mestrado em Engenharia Electrotécnica e de Computadores - Área de Especialização em Automação e Sistemas

Otimização da Amostragem e Estudo de Viabilidade da Monitorização de Sondas na ETAR de Rabada

Mestrado em Engenharia Química - Tecnologias de Protecção Ambiental

CMOS indoor light energy harvesting system for wireless sensing applications

This book discusses in detail the CMOS implementation of energy harvesting. The authors describe an integrated, indoor light energy harvesting system, based on a controller circuit that dynamically and automatically adjusts its operation to meet the actual light circumstances of the environment where the system is placed. The system is intended to power a sensor node, enabling an autonomous wireless sensor network (WSN). Although designed to cope with indoor light levels, the system is also able to work with higher levels, making it an all-round light energy harvesting system. The discussion includes experimental data obtained from an integrated manufactured prototype, which in conjunction with a photovoltaic (PV) cell, serves as a proof of concept of the desired energy harvesting system. © 2016 Springer International Publishing. All rights are reserved.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Utilização de métodos ópticos de correlação de imagem para a determinação da tenacidade à fractura de adesivos estruturais

Mestrado em Engenharia Mecânica

Historically accurate reconstructions and characterisation of chrome yellow pigments

Dissertation presented at the Faculty of Sciences and Technology of the New University of Lisbon in fulfillment of the requirements for the Master degree in Conservation Science

Resistência ao fogo de alvenarias sem função estrutural

Um incêndio é algo difícil de prever, assim como a sua consequência nos elementos de construção. Dessa forma, ao longo das últimas décadas, os elementos de construção têm sido alvo de diversos estudos a fim de avaliar os seus comportamentos quando solicitados em situação de incêndio. O International Building Code (IBC) descreve um método de cálculo analítico para a determinação da resistência ao fogo dos elementos da construção de acordo com os procedimentos de teste estabelecidos na ASTM E119 (Standard Test Methods for Fire Tests of Building Construction and Materials). Nesta dissertação foi feita uma análise desse método, que se mostrou inadequado para estimar a resistência ao fogo das alvenarias, sem função estrutural, de blocos cerâmicos e blocos de betão, uma vez que despreza qualquer efeito do ar no interior das mesmas. No seguimento desta análise, é apresentado um desenvolvimento do método descrito tendo em conta o efeito do ar. Depois de uma análise aos vários tipos de blocos cerâmicos e de betão com diferentes dimensões e geometrias foi possível obter uma relação entre a espessura equivalente de ar existente num bloco e a sua respectiva resistência ao fogo, de modo a serem obtidos os valores descritos na normalização existente. O efeito do ar mostrou ter uma maior influência na resistência ao fogo nas alvenarias constituídas por blocos cerâmicos de furação vertical, já que a sua geometria caracterizada por um elevado número de pequenos alvéolos contribui para o aumento do isolamento térmico, e consequentemente da sua resistência ao fogo. Nas alvenarias rebocadas os valores da resistência ao fogo aumentam cerca de 50%, quando revestidos com argamassa de cimento, e 70% quando revestidos com gesso, logo, o emprego de revestimentos representam uma boa alternativa para aumentar a resistência ao fogo.

Comércio bilateral Portugal - Brasil

Dissertação apresentada ao Instituto Superior de Contabilidade e Administração do Porto para obtenção do Grau de Mestre em Empreendedorismo e Internacionalização Orientadora: Professora Doutora Celsa Maria de Carvalho Machado

Parametric analog signal amplification applied to nanoscale cmos wireless digital transceivers

Thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Electrical and Computer Engineering by the Universidade Nova de Lisboa,Faculdade de Ciências e Tecnologia

Impact of Distributed Generation in the Transmission System Expansion Planning

In this work, the impact of distributed generation in the transmission expansion planning will be simulated through the performance of an optimization process for three different scenarios: the first without distributed generation, the second with distributed generation equivalent to 1% of the load, and the third with 5% of distributed generation. For modeling the expanding problem the load flow linearized method using genetic algorithms for optimization has been chosen. The test circuit used is a simplification of the south eastern Brazilian electricity system with 46 buses.