Dynamic-Data Driven Real-Time Identification for Electric Power Systems

Power system engineers face a double challenge: to operate electric power systems within narrow stability and security margins, and to maintain high reliability. There is an acute need to better understand the dynamic nature of power systems in order to be prepared for critical situations as they arise. Innovative measurement tools, such as phasor measurement units, can capture not only the slow variation of the voltages and currents but also the underlying oscillations in a power system. Such dynamic data accessibility provides us a strong motivation and a useful tool to explore dynamic-data driven applications in power systems. To fulfill this goal, this dissertation focuses on the following three areas: Developing accurate dynamic load models and updating variable parameters based on the measurement data, applying advanced nonlinear filtering concepts and technologies to real-time identification of power system models, and addressing computational issues by implementing the balanced truncation method. By obtaining more realistic system models, together with timely updated parameters and stochastic influence consideration, we can have an accurate portrait of the ongoing phenomena in an electrical power system. Hence we can further improve state estimation, stability analysis and real-time operation.

Combustion Instability and Active Control: Alternative Fuels, Augmentors, and Modeling Heat Release

Experimental and analytical studies were conducted to explore thermo-acoustic coupling during the onset of combustion instability in various air-breathing combustor configurations. These include a laboratory-scale 200-kW dump combustor and a 100-kW augmentor featuring a v-gutter flame holder. They were used to simulate main combustion chambers and afterburners in aero engines, respectively. The three primary themes of this work includes: 1) modeling heat release fluctuations for stability analysis, 2) conducting active combustion control with alternative fuels, and 3) demonstrating practical active control for augmentor instability suppression. The phenomenon of combustion instabilities remains an unsolved problem in propulsion engines, mainly because of the difficulty in predicting the fluctuating component of heat release without extensive testing. A hybrid model was developed to describe both the temporal and spatial variations in dynamic heat release, using a separation of variables approach that requires only a limited amount of experimental data. The use of sinusoidal basis functions further reduced the amount of data required. When the mean heat release behavior is known, the only experimental data needed for detailed stability analysis is one instantaneous picture of heat release at the peak pressure phase. This model was successfully tested in the dump combustor experiments, reproducing the correct sign of the overall Rayleigh index as well as the remarkably accurate spatial distribution pattern of fluctuating heat release. Active combustion control was explored for fuel-flexible combustor operation using twelve different jet fuels including bio-synthetic and Fischer-Tropsch types. Analysis done using an actuated spray combustion model revealed that the combustion response times of these fuels were similar. Combined with experimental spray characterizations, this suggested that controller performance should remain effective with various alternative fuels. Active control experiments validated this analysis while demonstrating 50-70\% reduction in the peak spectral amplitude. A new model augmentor was built and tested for combustion dynamics using schlieren and chemiluminescence techniques. Novel active control techniques including pulsed air injection were implemented and the results were compared with the pulsed fuel injection approach. The pulsed injection of secondary air worked just as effectively for suppressing the augmentor instability, setting up the possibility of more efficient actuation strategy.

Large-scale dynamic hydrofracturing, healing and fracture network characterization

Permeability of a rock is a dynamic property that varies spatially and temporally. Fractures provide the most efficient channels for fluid flow and thus directly contribute to the permeability of the system. Fractures usually form as a result of a combination of tectonic stresses, gravity (i.e. lithostatic pressure) and fluid pressures. High pressure gradients alone can cause fracturing, the process which is termed as hydrofracturing that can determine caprock (seal) stability or reservoir integrity. Fluids also transport mass and heat, and are responsible for the formation of veins by precipitating minerals within open fractures. Veining (healing) thus directly influences the rock’s permeability. Upon deformation these closed factures (veins) can refracture and the cycle starts again. This fracturing-healing-refacturing cycle is a fundamental part in studying the deformation dynamics and permeability evolution of rock systems. This is generally accompanied by fracture network characterization focusing on network topology that determines network connectivity. Fracture characterization allows to acquire quantitative and qualitative data on fractures and forms an important part of reservoir modeling. This thesis highlights the importance of fracture-healing and veins’ mechanical properties on the deformation dynamics. It shows that permeability varies spatially and temporally, and that healed systems (veined rocks) should not be treated as fractured systems (rocks without veins). Field observations also demonstrate the influence of contrasting mechanical properties, in addition to the complexities of vein microstructures that can form in low-porosity and permeability layered sequences. The thesis also presents graph theory as a characterization method to obtain statistical measures on evolving network connectivity. It also proposes what measures a good reservoir should have to exhibit potentially large permeability and robustness against healing. The results presented in the thesis can have applications for hydrocarbon and geothermal reservoir exploration, mining industry, underground waste disposal, CO2 injection or groundwater modeling.

Multi-Scale Dynamic Study of Secondary Impact During Drop Testing of Surface Mount Packages

This dissertation focuses on design challenges caused by secondary impacts to printed wiring assemblies (PWAs) within hand-held electronics due to accidental drop or impact loading. The continuing increase of functionality, miniaturization and affordability has resulted in a decrease in the size and weight of handheld electronic products. As a result, PWAs have become thinner and the clearances between surrounding structures have decreased. The resulting increase in flexibility of the PWAs in combination with the reduced clearances requires new design rules to minimize and survive possible internal collisions impacts between PWAs and surrounding structures. Such collisions are being termed ‘secondary impact’ in this study. The effect of secondary impact on board-level drop reliability of printed wiring boards (PWBs) assembled with MEMS microphone components, is investigated using a combination of testing, response and stress analysis, and damage modeling. The response analysis is conducted using a combination of numerical finite element modeling and simplified analytic models for additional parametric sensitivity studies.

Computational modeling of laser-induced delamination testing of thin films

Thin film adhesion often determines microelectronic device reliability and it is therefore essential to have experimental techniques that accurately and efficiently characterize it. Laser-induced delamination is a novel technique that uses laser-generated stress waves to load thin films at high strain rates and extract the fracture toughness of the film/substrate interface. The effectiveness of the technique in measuring the interface properties of metallic films has been documented in previous studies. The objective of the current effort is to model the effect of residual stresses on the dynamic delamination of thin films. Residual stresses can be high enough to affect the crack advance and the mode mixity of the delimitation event, and must therefore be adequately modeled to make accurate and repeatable predictions of fracture toughness. The equivalent axial force and bending moment generated by the residual stresses are included in a dynamic, nonlinear finite element model of the delaminating film, and the impact of residual stresses on the final extent of the interfacial crack, the relative contribution of shear failure, and the deformed shape of the delaminated film is studied in detail. Another objective of the study is to develop techniques to address issues related to the testing of polymeric films. These type of films adhere well to silicon and the resulting crack advance is often much smaller than for metallic films, making the extraction of the interface fracture toughness more difficult. The use of an inertial layer which enhances the amount of kinetic energy trapped in the film and thus the crack advance is examined. It is determined that the inertial layer does improve the crack advance, although in a relatively limited fashion. The high interface toughness of polymer films often causes the film to fail cohesively when the crack front leaves the weakly bonded region and enters the strong interface. The use of a tapered pre-crack region that provides a more gradual transition to the strong interface is examined. The tapered triangular pre-crack geometry is found to be effective in reducing the stresses induced thereby making it an attractive option. We conclude by studying the impact of modifying the pre-crack geometry to enable the testing of multiple polymer films.

Sorghum Crop Modeling and Its Utility in Agronomy and Breeding

Crop models are simplified mathematical representations of the interacting biological and environmental components of the dynamic soil–plant–environment system. Sorghum crop modeling has evolved in parallel with crop modeling capability in general, since its origins in the 1960s and 1970s. Here we briefly review the trajectory in sorghum crop modeling leading to the development of advanced models. We then (i) overview the structure and function of the sorghum model in the Agricultural Production System sIMulator (APSIM) to exemplify advanced modeling concepts that suit both agronomic and breeding applications, (ii) review an example of use of sorghum modeling in supporting agronomic management decisions, (iii) review an example of the use of sorghum modeling in plant breeding, and (iv) consider implications for future roles of sorghum crop modeling. Modeling and simulation provide an avenue to explore consequences of crop management decision options in situations confronted with risks associated with seasonal climate uncertainties. Here we consider the possibility of manipulating planting configuration and density in sorghum as a means to manipulate the productivity–risk trade-off. A simulation analysis of decision options is presented and avenues for its use with decision-makers discussed. Modeling and simulation also provide opportunities to improve breeding efficiency by either dissecting complex traits to more amenable targets for genetics and breeding, or by trait evaluation via phenotypic prediction in target production regions to help prioritize effort and assess breeding strategies. Here we consider studies on the stay-green trait in sorghum, which confers yield advantage in water-limited situations, to exemplify both aspects. The possible future roles of sorghum modeling in agronomy and breeding are discussed as are opportunities related to their synergistic interaction. The potential to add significant value to the revolution in plant breeding associated with genomic technologies is identified as the new modeling frontier.

A Modeling Approach to Quantify the Effects of Stomatal Behavior and Mesophyll Conductance on Leaf Water Use Efficiency

Water use efficiency (WUE) is considered as a determinant of yield under stress and a component of crop drought resistance. Stomatal behavior regulates both transpiration rate and net assimilation and has been suggested to be crucial for improving crop WUE. In this work, a dynamic model was used to examine the impact of dynamic properties of stomata on WUE. The model includes sub-models of stomatal conductance dynamics, solute accumulation in the mesophyll, mesophyll water content, and water flow to the mesophyll. Using the instantaneous value of stomatal conductance, photosynthesis, and transpiration rate were simulated using a biochemical model and Penman-Monteith equation, respectively. The model was parameterized for a cucumber leaf and model outputs were evaluated using climatic data. Our simulations revealed that WUE was higher on a cloudy than a sunny day. Fast stomatal reaction to light decreased WUE during the period of increasing light (e.g., in the morning) by up to 10.2% and increased WUE during the period of decreasing light (afternoon) by up to 6.25%. Sensitivity of daily WUE to stomatal parameters and mesophyll conductance to CO2 was tested for sunny and cloudy days. Increasing mesophyll conductance to CO2 was more likely to increase WUE for all climatic conditions (up to 5.5% on the sunny day) than modifications of stomatal reaction speed to light and maximum stomatal conductance.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Object-Oriented Modeling Simulation and Control of Activated Sludge Process

Object-oriented modeling is spreading in current simulation of wastewater treatments plants through the use of the individual components of the process and its relations to define the underlying dynamic equations. In this paper, we describe the use of the free-software OpenModelica simulation environment for the object-oriented modeling of an activated sludge process under feedback control. The performance of the controlled system was analyzed both under normal conditions and in the presence of disturbances. The object-oriented described approach represents a valuable tool in teaching provides a practical insight in wastewater process control field.

Transfer function-noise modeling and spatial interpolation to evaluate the risk of extreme (shallow) water-table levels in the Brazilian Cerrados

Water regimes in the Brazilian Cerrados are sensitive to climatological disturbances and human intervention. The risk that critical water-table levels are exceeded over long periods of time can be estimated by applying stochastic methods in modeling the dynamic relationship between water levels and driving forces such as precipitation and evapotranspiration. In this study, a transfer function-noise model, the so called PIRFICT-model, is applied to estimate the dynamic relationship between water-table depth and precipitation surplus/deficit in a watershed with a groundwater monitoring scheme in the Brazilian Cerrados. Critical limits were defined for a period in the Cerrados agricultural calendar, the end of the rainy season, when extremely shallow levels (< 0.5-m depth) can pose a risk to plant health and machinery before harvesting. By simulating time-series models, the risk of exceeding critical thresholds during a continuous period of time (e.g. 10 days) is described by probability levels. These simulated probabilities were interpolated spatially using universal kriging, incorporating information related to the drainage basin from a digital elevation model. The resulting map reduced model uncertainty. Three areas were defined as presenting potential risk at the end of the rainy season. These areas deserve attention with respect to water-management and land-use planning.

Causal factors of anxiety symptoms in children

Aim: The present work aimed to investigate the impact of the child’s cognitions associated with ambiguous stimuli that refer to anxiety, both parents’ fears and anxiety, and parents’ attributions to the child’s interpretations of ambiguous stimuli on child anxiety. The influence of parental modelling on child’s cognitions was also analyzed. Method: The final sample was composed of 111 children (62 boys; 49 girls) with ages between 10 and 11 years (M = 10.6, SD = 0.5) from a community population, and both their parents. The variables identified as most significant were included in a predictive model of anxiety. Results: Results revealed the children’s thoughts (positive and negative) related to ambiguous stimuli that describe anxiety situations. Parents’ fears and mothers’ anxiety significantly predict children’s anxiety. Those variables explain 29% of the variance in children general anxiety. No evidence was found for a direct parental modeling of child cognitions. Conclusion: Children’s positive thoughts seem to be cognitive aspects that buffer against anxiety. Negative thoughts are vulnerability factors for the development of child anxiety. Parents’ fears and anxiety should be analyzed in separate as they have distinct influences over children’s anxiety. Mothers’ fears contribute to children’s anxiety by reducing it, revealing a possible protective effect. It is suggested that the contribution of both parents’ fears to children’s anxiety may be interpreted acknowledging the existence of “psychological and/or behavioral filters”. Mothers’ filters seem to be well developed while fathers’ filters seem to be compromised. The contribution of mothers’ anxiety (but not fathers’ anxiety) to children’s anxiety is also understood in light of the possible existence of a “proximity space” between the child and parents, which is wider with mothers than with fathers.

Análise de volatilidade, integração de preços e previsibilidade para o mercado brasileiro de camarão

The present paper has the purpose of investigate the dynamics of the volatility structure in the shrimp prices in the Brazilian fish market. Therefore, a description of the initial aspects of the shrimp price series was made. From this information, statistics tests were made and selected univariate models to be price predictors. Then, it was verified the existence of relationship of long-term equilibrium between the Brazilian and American imported shrimp and if, confirmed the relationship, whether or not there is a causal link between these assets, considering that the two countries had presented trade relations over the years. It is presented as an exploratory research of applied nature with quantitative approach. The database was collected through direct contact with the Companhia de Entrepostos e Armazéns Gerais de São Paulo (CEAGESP) and on the official website of American import, National Marine Fisheries Service - National Oceanic and Atmospheric Administration (NMFS- NOAA). The results showed that the great variability in the active price is directly related with the gain and loss of the market agents. The price series presents a strong seasonal and biannual effect. The average structure of price of shrimp in the last 12 years was R$ 11.58 and external factors besides the production and marketing (U.S. antidumping, floods and pathologies) strongly affected the prices. Among the tested models for predicting prices of shrimp, four were selected, which through the prediction methodologies of one step forward of horizon 12, proved to be statistically more robust. It was found that there is weak evidence of long-term equilibrium between the Brazilian and American shrimp, where equivalently, was not found a causal link between them. We concluded that the dynamic pricing of commodity shrimp is strongly influenced by external productive factors and that these phenomena cause seasonal effects in the prices. There is no relationship of long-term stability between the Brazilian and American shrimp prices, but it is known that Brazil imports USA production inputs, which somehow shows some dependence productive. To the market agents, the risk of interferences of the external prices cointegrated to Brazilian is practically inexistent. Through statistical modeling is possible to minimize the risk and uncertainty embedded in the fish market, thus, the sales and marketing strategies for the Brazilian shrimp can be consolidated and widespread

Reasoning on the Risks of Dynamic Manufacturing Networks through Cognitive Mapping

Part 17: Risk Analysis

Photovoltaic power forecast modeling with artificial neural networks

Dissertação de Mestrado, Engenharia Eletrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2016

Modeling the Effect of Vapor Wall Deposition on the Formation of Secondary Organic Aerosol in Chamber Studies

Laboratory chamber experiments are used to investigate formation of secondary organic aerosol (SOA) from biogenic and anthropogenic precursors under a variety of environmental conditions. Simulations of these experiments test our understanding of the prevailing chemistry of SOA formation as well as the dynamic processes occurring in the chamber itself. One dynamic process occurring in the chamber that was only recently recognized is the deposition of vapor species to the Teflon walls of the chamber. Low-volatility products formed from the oxidation of volatile organic compounds (VOCs) deposit on the walls rather than forming SOA, decreasing the amount of SOA formed (quantified as the SOA yield: mass of SOA formed per mass of VOC reacted). In this work, several modeling studies are presented that address the effect of vapor wall deposition on SOA formation in chambers.

A coupled vapor-particle dynamics model is used to examine the competition among the rates of gas-phase oxidation to low volatility products, wall deposition of these products, and mass transfer to the particle phase. The relative time scales of these rates control the amount of SOA formed by affecting the influence of vapor wall deposition. Simulations show that an effect on SOA yield of changing the vapor-particle mass transfer rate is only observed when SOA formation is kinetically limited. For systems with kinetically limited SOA formation, increasing the rate of vapor-particle mass transfer by increasing the concentration of seed particles is an effective way to minimize the effect of vapor wall deposition.

This coupled vapor-particle dynamics model is then applied to α-pinene ozonolysis SOA experiments. Experiments show that the SOA yield is affected when changing the oxidation rate but not when changing the rate of gas-particle mass transfer by changing the concentration of seed particles. Model simulations show that the absence of an effect of changing the seed particle concentration is consistent with SOA formation being governed by quasi-equilibrium growth, in which gas-particle equilibrium is established much faster than the rate of change of the gas-phase concentration. The observed effect of oxidation rate on SOA yield arises due to the presence of vapor wall deposition: gas-phase oxidation products are produced more quickly and condense preferentially onto seed particles before being lost to the walls. Therefore, for α-pinene ozonolysis, increasing the oxidation rate is the most effective way to mitigate the influence of vapor wall deposition.

Finally, the detailed model GECKO-A (Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere) is used to simulate α-pinene photooxidation SOA experiments. Unexpectedly, α-pinene OH oxidation experiments show no effect when changing either the oxidation rate or the vapor-particle mass transfer rate, whereas GECKO-A predicts that changing the oxidation rate should drastically affect the SOA yield. Sensitivity studies show that the assumed magnitude of the vapor wall deposition rate can greatly affect conclusions drawn from comparisons between simulations and experiments. If vapor wall loss in the Caltech chamber is of order 10^-5 s^-1, GECKO-A greatly overpredicts SOA during high UV experiments, likely due to an overprediction of second-generation products. However, if instead vapor wall loss in the Caltech chamber is of order 10^-3 s^-1, GECKO-A greatly underpredicts SOA during low UV experiments, possibly due to missing autoxidation pathways in the α-pinene mechanism.