Dynamic modeling and analysis of single-stage boost inverters under normal and abnormal conditions

Inverters play key roles in connecting sustainable energy (SE) sources to the local loads and the ac grid. Although there has been a rapid expansion in the use of renewable sources in recent years, fundamental research, on the design of inverters that are specialized for use in these systems, is still needed. Recent advances in power electronics have led to proposing new topologies and switching patterns for single-stage power conversion, which are appropriate for SE sources and energy storage devices. The current source inverter (CSI) topology, along with a newly proposed switching pattern, is capable of converting the low dc voltage to the line ac in only one stage. Simple implementation and high reliability, together with the potential advantages of higher efficiency and lower cost, turns the so-called, single-stage boost inverter (SSBI), into a viable competitor to the existing SE-based power conversion technologies.^ The dynamic model is one of the most essential requirements for performance analysis and control design of any engineering system. Thus, in order to have satisfactory operation, it is necessary to derive a dynamic model for the SSBI system. However, because of the switching behavior and nonlinear elements involved, analysis of the SSBI is a complicated task.^ This research applies the state-space averaging technique to the SSBI to develop the state-space-averaged model of the SSBI under stand-alone and grid-connected modes of operation. Then, a small-signal model is derived by means of the perturbation and linearization method. An experimental hardware set-up, including a laboratory-scaled prototype SSBI, is built and the validity of the obtained models is verified through simulation and experiments. Finally, an eigenvalue sensitivity analysis is performed to investigate the stability and dynamic behavior of the SSBI system over a typical range of operation. ^

(Table 1) Age determination of composite sediment record LO09-14

A sediment core from Reykjanes Ridge has been studied at 10- to 50-year time resolution to document variability of Holocene surface water conditions in the western North Atlantic and to evaluate effects of Holocene ice-rafting episodes. Diatom assemblages are converted to quantitative sea surface temperatures (SST) using three different transfer functions. Spectral and scale-space methods are also applied on the records to explore variability at different timescales. Diatom assemblage and SST records clearly show that decaying remnants of the Laurentide ice sheet strongly influenced early Holocene climate in the western North Atlantic. This overrode the predominance of Milankovitch forcing, which played a key role in the development of Holocene climate in the eastern North Atlantic and Nordic Seas. Superimposed on general Holocene climate change is high-frequency SST variability on the order of 1°-3°C. The record also documents climatic oscillations with 600- to 1000-, ~1500-, and 2500-year periodicities, with a time-dependent dominance of different periodicities through the Holocene; a clear change in variability occurred about 5 ka BP. The SST record also provides evidence for Holocene cooling events (HCE) that, in some cases, correlate to documented southward intrusions of ice into the North Atlantic.

Advances in Dynamic Modeling and Computation for Count Data

A class of multi-process models is developed for collections of time indexed count data. Autocorrelation in counts is achieved with dynamic models for the natural parameter of the binomial distribution. In addition to modeling binomial time series, the framework includes dynamic models for multinomial and Poisson time series. Markov chain Monte Carlo (MCMC) and Po ́lya-Gamma data augmentation (Polson et al., 2013) are critical for fitting multi-process models of counts. To facilitate computation when the counts are high, a Gaussian approximation to the P ́olya- Gamma random variable is developed.

Three applied analyses are presented to explore the utility and versatility of the framework. The first analysis develops a model for complex dynamic behavior of themes in collections of text documents. Documents are modeled as a “bag of words”, and the multinomial distribution is used to characterize uncertainty in the vocabulary terms appearing in each document. State-space models for the natural parameters of the multinomial distribution induce autocorrelation in themes and their proportional representation in the corpus over time.

The second analysis develops a dynamic mixed membership model for Poisson counts. The model is applied to a collection of time series which record neuron level firing patterns in rhesus monkeys. The monkey is exposed to two sounds simultaneously, and Gaussian processes are used to smoothly model the time-varying rate at which the neuron’s firing pattern fluctuates between features associated with each sound in isolation.

The third analysis presents a switching dynamic generalized linear model for the time-varying home run totals of professional baseball players. The model endows each player with an age specific latent natural ability class and a performance enhancing drug (PED) use indicator. As players age, they randomly transition through a sequence of ability classes in a manner consistent with traditional aging patterns. When the performance of the player significantly deviates from the expected aging pattern, he is identified as a player whose performance is consistent with PED use.

All three models provide a mechanism for sharing information across related series locally in time. The models are fit with variations on the P ́olya-Gamma Gibbs sampler, MCMC convergence diagnostics are developed, and reproducible inference is emphasized throughout the dissertation.

Bayesian Emulation for Sequential Modeling, Inference and Decision Analysis

The advances in three related areas of state-space modeling, sequential Bayesian learning, and decision analysis are addressed, with the statistical challenges of scalability and associated dynamic sparsity. The key theme that ties the three areas is Bayesian model emulation: solving challenging analysis/computational problems using creative model emulators. This idea defines theoretical and applied advances in non-linear, non-Gaussian state-space modeling, dynamic sparsity, decision analysis and statistical computation, across linked contexts of multivariate time series and dynamic networks studies. Examples and applications in financial time series and portfolio analysis, macroeconomics and internet studies from computational advertising demonstrate the utility of the core methodological innovations.

Chapter 1 summarizes the three areas/problems and the key idea of emulating in those areas. Chapter 2 discusses the sequential analysis of latent threshold models with use of emulating models that allows for analytical filtering to enhance the efficiency of posterior sampling. Chapter 3 examines the emulator model in decision analysis, or the synthetic model, that is equivalent to the loss function in the original minimization problem, and shows its performance in the context of sequential portfolio optimization. Chapter 4 describes the method for modeling the steaming data of counts observed on a large network that relies on emulating the whole, dependent network model by independent, conjugate sub-models customized to each set of flow. Chapter 5 reviews those advances and makes the concluding remarks.

Effect of Termination of Long-term Free Air CO2 Enrichment on Physiology and Carbon Allocation in a Loblolly Pine Dominated Forest

This dissertation examined the response to termination of CO2 enrichment of a forest ecosystem exposed to long-term elevated atmospheric CO2 condition, and aimed at investigating responses and their underlying mechanisms of two important factors of carbon cycle in the ecosystem, stomatal conductance and soil respiration. Because the contribution of understory vegetation to the entire ecosystem grew with time, we first investigated the effect of elevated CO2 on understory vegetation. Potential growth enhancing effect of elevated CO2 were not observed, and light seemed to be a limiting factor. Secondly, we examined the importance of aerodynamic conductance to determine canopy conductance, and found that its effect can be negligible. Responses of stomatal conductance and soil respiration were assessed using Bayesian state space model. In two years after the termination of CO2 enrichment, stomatal conductance in formerly elevated CO2 returned to ambient level, while soil respiration became smaller than ambient level and did not recovered to ambient in two years.

An impulsive differential equation model for Marek's disease

Many dynamical processes are subject to abrupt changes in state. Often these perturbations can be periodic and of short duration relative to the evolving process. These types of phenomena are described well by what are referred to as impulsive differential equations, systems of differential equations coupled with discrete mappings in state space. In this thesis we employ impulsive differential equations to model disease transmission within an industrial livestock barn. In particular we focus on the poultry industry and a viral disease of poultry called Marek's disease. This system lends itself well to impulsive differential equations. Entire cohorts of poultry are introduced and removed from a barn concurrently. Additionally, Marek's disease is transmitted indirectly and the viral particles can survive outside the host for weeks. Therefore, depopulating, cleaning, and restocking of the barn are integral factors in modelling disease transmission and can be completely captured by the impulsive component of the model. Our model allows us to investigate how modern broiler farm practices can make disease elimination difficult or impossible to achieve. It also enables us to investigate factors that may contribute to virulence evolution. Our model suggests that by decrease the cohort duration or by decreasing the flock density, Marek's disease can be eliminated from a barn with no increase in cleaning effort. Unfortunately our model also suggests that these practices will lead to disease evolution towards greater virulence. Additionally, our model suggests that if intensive cleaning between cohorts does not rid the barn of disease, it may drive evolution and cause the disease to become more virulent.

Programmazione ad Agenti e Pianificazione: AgentSpeak(PL) come caso di studio

In questa tesi si discutono inizialmente i concetti chiave di agente e sistema multi-agente e si descrivono in ogni dettaglio il linguaggio di programmazione AgentSpeak(L) e la piattaforma Jason, fornendo le basi per poter programmare con il paradigma AOP. Lo scopo centrale di questa tesi è quello di estendere il modello di pianificazione dell’interprete di AgentSpeak(L), considerato come caso specifico, con un approccio che può essere integrato in qualsiasi linguaggio di programmazione ad agenti. Si espone un’evoluzione di AgentSpeak(L) in AgentSpeak(PL), ossia la creazione ed esecuzione di piani automatici in caso di fallimento attraverso l'uso di un algoritmo di planning state-space. L'approccio integrativo modifica il Ciclo di Reasoning di Jason proponendo in fase di pianificazione automatica un riuso di piani già esistenti, atto a favorire la riduzione di tempi e costi nel long-term in un sistema multi-agente. Nel primo capitolo si discute della nozione di agente e delle sue caratteristiche principali mentre nel secondo capitolo come avviene la vera e propria programmazione con AgentSpeak(L). Avendo approfondito questi argomenti base, il terzo capitolo è incentrato sull’interprete Jason e il quarto su una migliore estensione dell'interprete, in grado di superare i limiti migliorando le performance nel tempo. Si delineano infine alcune considerazioni e ringraziamenti nel quinto e ultimo capitolo. Viene proposta con scrittura di carattere divulgativo e non ambiguo.

Constructing stability landscapes to identify alternative states in coupled social-ecological agent-based models

The resilience of a social-ecological system is measured by its ability to retain core functionality when subjected to perturbation. Resilience is contextually dependent on the state of system components, the complex interactions among these components, and the timing, location, and magnitude of perturbations. The stability landscape concept provides a useful framework for considering resilience within the specified context of a particular social-ecological system but has proven difficult to operationalize. This difficulty stems largely from the complex, multidimensional nature of the systems of interest and uncertainty in system response. Agent-based models are an effective methodology for understanding how cross-scale processes within and across social and ecological domains contribute to overall system resilience. We present the results of a stylized model of agricultural land use in a small watershed that is typical of the Midwestern United States. The spatially explicit model couples land use, biophysical models, and economic drivers with an agent-based model to explore the effects of perturbations and policy adaptations on system outcomes. By applying the coupled modeling approach within the resilience and stability landscape frameworks, we (1) estimate the sensitivity of the system to context-specific perturbations, (2) determine potential outcomes of those perturbations, (3) identify possible alternative states within state space, (4) evaluate the resilience of system states, and (5) characterize changes in system-scale resilience brought on by changes in individual land use decisions.

Structural Time Series Modeling: an Application to Environmental Variables

A structural time series model is one which is set up in terms of components which have a direct interpretation. In this paper, the discussion focuses on the dynamic modeling procedure based on the state space approach (associated to the Kalman filter), in the context of surface water quality monitoring, in order to analyze and evaluate the temporal evolution of the environmental variables, and thus identify trends or possible changes in water quality (change point detection). The approach is applied to environmental time series: time series of surface water quality variables in a river basin. The statistical modeling procedure is applied to monthly values of physico- chemical variables measured in a network of 8 water monitoring sites over a 15-year period (1999-2014) in the River Ave hydrological basin located in the Northwest region of Portugal.

A noise simulator for eLISA: Migrating LISA Pathfinder knowledge to the eLISA mission

We present a new technical simulator for the eLISA mission, based on state space modeling techniques and developed in MATLAB. This simulator computes the coordinate and velocity over time of each body involved in the constellation, i.e. the spacecraft and its test masses, taking into account the different disturbances and actuations. This allows studying the contribution of instrumental noises and system imperfections on the residual acceleration applied on the TMs, the latter reflecting the performance of the achieved free-fall along the sensitive axis. A preliminary version of the results is presented.

Análisis y simulación de un rectificador trifásico controlado Active Front End (AFE)

Este artículo presenta un resultado de investigación financiado con recursos propios en el que se expone un modelo en espacio de estados de un rectificador trifásico controlado active front end. Utilizando este modelo se deriva una ley de control orientado al voltaje (VOC), enfocado en el comportamiento como carga resistiva, factor de potencia unitario, el cual es probado mediante simulación usando el Toolbox SimPowerSystem en Simulink de Matlab®.

Modeling perspective for the relevant market of voice services: mobile to mobile

Los mercados asociados a los servicios de voz móvil a móvil, brindados por operadoras del Sistema Móvil Avanzado en Latinoamérica, han estado sujetos a procesos regulatorios motivados por la dominancia en el mercado de un operador, buscando obtener óptimas condiciones de competencia. Específicamente en Ecuador, la Superintendencia de Telecomunicaciones (Organismo Técnico de Control de Telecomunicaciones) desarrolló un modelo para identificar acciones de regulación que puedan proporcionar al mercado efectos sostenibles de competencia en el largo plazo. Este artículo trata sobre la aplicación de la ingeniería de control para desarrollar un modelo integral del mercado, empleando redes neuronales para la predicción de trarifas de cada operador y un modelo de lógica difusa para predecir la demanda. Adicionalmente, se presenta un modelo de inferencia de lógica difusa para reproducir las estrategias de mercadeo de los operadores y la influencia sobre las tarifas. Dichos modelos permitirían la toma adecuada de decisiones y fueron validados con datos reales.

Towards a predictive model of Ca²⁺ puffs

We investigate key characteristics of Ca²⁺ puffs in deterministic and stochastic frameworks that all incorporate the cellular morphology of IP[subscript]3 receptor channel clusters. In a first step, we numerically study Ca²⁺ liberation in a three dimensional representation of a cluster environment with reaction-diffusion dynamics in both the cytosol and the lumen. These simulations reveal that Ca²⁺ concentrations at a releasing cluster range from 80 µM to 170 µM and equilibrate almost instantaneously on the time scale of the release duration. These highly elevated Ca²⁺ concentrations eliminate Ca²⁺ oscillations in a deterministic model of an IP[subscript]3R channel cluster at physiological parameter values as revealed by a linear stability analysis. The reason lies in the saturation of all feedback processes in the IP[subscript]3R gating dynamics, so that only fluctuations can restore experimentally observed Ca²⁺ oscillations. In this spirit, we derive master equations that allow us to analytically quantify the onset of Ca²⁺ puffs and hence the stochastic time scale of intracellular Ca²⁺ dynamics. Moving up the spatial scale, we suggest to formulate cellular dynamics in terms of waiting time distribution functions. This approach prevents the state space explosion that is typical for the description of cellular dynamics based on channel states and still contains information on molecular fluctuations. We illustrate this method by studying global Ca²⁺ oscillations.

Epidemiological features of patients with nonsyndromic cleft lip and/or palate in Western Parana

Aim:To describe the clinical, demographic and environmental features associated with NSCL/P (nonsyndromic cleft lip and/or palate) patients born in western Parana state, Brazil. Methods: This cross-sectional, observational, retrospective study included 188 patients attended at the Association of Carriers of Cleft Lip and Palate - APOFILAB, Cascavel-Parana, between 2012 and 2014. Information on demographic characteristics, medical and dental histories and life style factors were obtained from records and personal interviews. Results: Among the 188 patients, cleft lip and palate (CLP) was the most frequent subtype (55.8%), followed by cleft lip only (CLO, 25.0%) and cleft palate only (CPO, 19.2%). Caucasian males were the most affected, although no differences among types of cleft were observed. The otorhinolaryngologic and respiratory alterations were the most frequent systemic alterations in NSCL/P patients, and more than 80% of the NSCL/P mothers reported no vitamin supplements during the first trimester of pregnancy. Conclusions: This study revealed that the prevalence of nonsyndromic oral cleft types in this cohort was quite similar to previously reported prevalence rates. Systemic alterations were identified among 23.4% of the patients and patients with CLP were the most affected. History of maternal exposition to environmental factors related to nonsyndromic oral clefts was frequent and most mothers reported no vitamin supplements during the pregnancy. This study highlights the importance of identifying systemic alterations and risk factors associated with NSCL/P in the Brazilian population for planning comprehensive strategies and integrated actions for the development of preventive programs and treatment.

Credit risk & forward price models

Doutoramento em Gestão