<p>The electric arc furnace (EAF) is a highly energy intensive process used to convert scrap metal into molten steel. The aim of this research is to develop a dynamic model of an industrial EAF process, and investigate its application for optimal EAF operation. This work has three main contributions; the first contribution is developing a model largely based on MacRosty and Swartz (2005) to meet the operation of a new industrial partner (ArcelorMittal Contrecoeur Ouest, Quebec, Canada). The second contribution is carrying out sensitivity analyses to investigate the effect of the scrap components on the EAF process. Finally, the third contribution includes the development of a constrained multi-rate extended Kalman filter (EKF) to infer the states of the system from the measurements provided by the plant.</p> <p>A multi-zone model is developed and discussed in detail. Heat and mass transfer relationships are considered. Chemical equilibrium is assumed in two of the zones and calculated through the minimization of the Gibbs free energy. The most sensitive parameters are identified and estimated using plant measurements. The model is then validated against plant data and has shown a reasonable level of accuracy.</p> <p>Local differential sensitivity analysis is performed to investigate the effect of scrap components on the EAF operation. Iron was found to have the greatest effect amongst the components present. Then, the optimal operation of the furnace is determined through economic optimization. In this case, the trade-off between electrical and chemical energy is determined in order to maximize the profit. Different scenarios are considered that include price variation in electricity, methane and oxygen.</p> <p>A constrained multi-rate EKF is implemented in order to estimate the states of the system using plant measurements. The EKF showed high performance in tracking the true states of the process, even in the presence of a parametric plant-model mismatch.</p> / Master of Applied Science (MASc)

<p>This thesis presents a family of automatic elastic registration methods applicable to single and multimodal images of similar or dissimilar dimensions. These registration algorithms employ a generic dynamic linear elastic continuum mechanics model of the tissue deformation which is discretized using the finite element method. The dynamic deformation model provides spatial and temporal correlation between images acquired from different orientations at different times. First, a volumetric registration algorithm is presented which estimates the deformation field by balancing internal deformation forces of the elastic model against external forces derived from an intensity-based similarity measure between images. The registration is achieved by iteratively solving a reduced form of the dynamic deformation equations in response to image-derived nodal forces. A general approach for automatic deformable image registration is also presented in this thesis which deals with different registration problems within a unified framework irrespective of the image modality and dimension. Using the dynamic deformation model, the problem of deformable image registration is approached as a classical state estimation problem with various image similarity measures providing an observation model. With this formulation, single and multiple-modality, 3D-3D and 3D-2D image registration problems can all be treated within the same framework.The registration is achieved through a Kalman-like filtering process which incorporates information from the deformation model and an observation error computed from an intensity-based similarity measure. Correlation ratio, normalized correlation coefficient, mutual information, modality independent neighborhood descriptor and sum of squared differences between images are similarity/distance measures employed for single and multiple modality image registration in this thesis</p> / Doctor of Philosophy (PhD)

The vision of the smart grid is predicated upon pervasive use of modern digital communication techniques in today's power system. As wide area measurements and control techniques are being developed and deployed for a more resilient power system, the role of communication networks is becoming prominent. Advanced communication infrastructure provides much wider system observability and enables globally optimal control schemes. Wide area measurement and monitoring with Phasor Measurement Units (PMUs) or Intelligent Electronic Devices (IED) is a growing trend in this context. However, the large amount of data collected by PMUs or IEDs needs to be transferred over the data network to control centers where real-time state estimation, protection, and control decisions are made. The volume and frequency of such data transfers, and real-time delivery requirements mandate that sufficient bandwidth and proper delay characteristics must be ensured for the correct operations. Power system dynamics get influenced by the underlying communication infrastructure. Therefore, extensive integration of power system and communication infrastructure mandates that the two systems be studied as a single distributed cyber-physical system. This dissertation proposes a global event-driven co-simulation framework, which is termed as GECO, for interconnected power system and communication network. GECO can be used as a design pattern for hybrid system simulation with continuous/discrete sub-components. An implementation of GECO is achieved by integrating two software packages: PSLF and NS2 into the framework. Besides, this dissertation proposes and studies a set of power system applications which can be only properly evaluated on a co-simulation framework like GECO, namely communication-based distance relay protection, all-PMU state estimation and PMU-based out-of-step protection. All of them take advantage of interplays between the power grid and the communication infrastructure. The GECO experiments described in this dissertation not only show the efficacy of the GECO framework, but also provide experience on how to go about using GECO in smart grid planning activities. / Ph. D.

With the advent of distributed energy resources (DERs), electric vehicles, and demand-response programs, grid operators are in dire need of new monitoring and design tools that help improve efficiency, reliability, and stability of modern power networks. To this end, the work in this thesis explores a generalized modeling and analysis framework for two pertinent tasks: i) learning loads via grid probing, and; ii) optimizing power grid topologies for stability. Distribution grids currently lack comprehensive real-time metering. Nevertheless, grid operators require precise knowledge of loads and renewable generation to accomplish any feeder optimization task. At the same time, new grid technologies, such as solar panels and energy storage units are interfaced via inverters with advanced sensing and actuation capabilities. In this context, we first put forth the idea of engaging power electronics to probe an electric grid and record its voltage response at actuated and metered buses to infer non-metered loads. Probing can be accomplished by commanding inverters to momentarily perturb their power injections. Multiple probing actions can be induced within a few tens of seconds. Load inference via grid probing is formulated as an implicit nonlinear system identification task, which is shown to be topologically observable under certain conditions. The analysis holds for single- and multi-phase grids, radial or meshed, and applies to phasor or magnitude-only voltage data. Using probing to learn non-constant-power loads is also analyzed as a special case. Once a probing setup is deemed topologically observable, a methodology for designing probing injections abiding by inverter and network constraints to improve load estimates is provided. The probing task under noisy phasor and non-phasor data is tackled using a semidefinite-program relaxation. As a second contribution, we also study the effect of topology on the linear time-invariant dynamics of power networks. For a variety of stability metrics, a unified framework based on the H2-norm of the system is presented. The proposed framework assesses the robustness of power grids to small disturbances and is used to study the optimal placement of new lines on existing networks as well as the design of radial topologies for new networks. / Doctor of Philosophy / Increased penetration of distributed energy resources such as solar panels, wind farms, and energy storage systems is forcing utilities to rethink how they design and operate their power networks. To ensure efficient and reliable operation of distribution networks and to perform any grid-wide optimization or dispatch tasks, the system operator needs to precisely know the net load (energy output) of every customer. However, due to the sheer extent of distribution networks (millions of customers) and low investment interest in the past, distribution grids have limited metering infrastructure. Nevertheless, data from grid sensors comprised of voltage and load measurements are readily available from a subset of customers at high temporal resolution. In addition, the smart inverters found in solar panels, energy storage units, and electric vehicles can be controlled within microseconds. The work in this thesis explores how the proliferation of grid sensors together with the controllability of smart inverters can be leveraged for inferring the non-metered loads i.e., energy output of customers that are not equipped with smart inverters/sensors. In addition to the load learning task, this thesis also presents a modeling and analysis framework to study the optimal design of topologies (how customers are electrically inter-connected) for improving stability of our power networks.

Indirect wind estimation onboard unmanned aerial systems (UASs) can be accomplished using existing air vehicle sensors along with a dynamic model of the UAS augmented with additional wind-related states. It is often desired to extract a mean component of the wind the from frequency fluctuations (i.e. turbulence). Commonly, a variation of the KALMAN filter is used, with explicit or implicit assumptions about the nature of the random wind velocity. This dissertation presents an H-infinity (H∞) filtering approach to wind estimation which requires no assumptions about the statistics of the process or measurement noise. To specify the wind frequency content of interest a low-pass filter is incorporated. We develop the augmented UAS model in continuous-time, derive the H∞ filter, and introduce a KALMAN-BUCY filter for comparison. The filters are applied to data gathered during UAS flight tests and validated using a vaned air data unit onboard the aircraft. The H∞ filter provides quantitatively better estimates of the wind than the KALMAN-BUCY filter, with approximately 10-40% less root-mean-square (RMS) error in the majority of cases. It is also shown that incorporating DRYDEN turbulence does not improve the KALMAN-BUCY results. Additionally, this dissertation describes the theory and process for using generalized polynomial chaos (gPC) to re-cast the dynamics of a system with non-deterministic parameters as a deterministic system. The concepts are applied to the problem of wind estimation and characterizing the precision of wind estimates over time due to known parametric uncertainties. A novel truncation method, known as Sensitivity-Informed Variable Reduction (SIVR) was developed. In the multivariate case presented here, gPC and the SIVR-derived reduced gPC (gPCr) exhibit a computational advantage over Monte Carlo sampling-based methods for uncertainty quantification (UQ) and sensitivity analysis (SA), with time reductions of 38% and 98%, respectively. Lastly, while many estimation approaches achieve desirable accuracy under the assumption of known system parameters, reducing the effect of parametric uncertainty on wind estimate precision is desirable and has not been thoroughly investigated. This dissertation describes the theory and process for combining gPC and H-infinity (H∞) filtering. In the multivariate case presented, the gPC H∞ filter shows superiority over a nominal H∞ filter in terms of variance in estimates due to model parametric uncertainty. The error due to parametric uncertainty, as characterized by the variance in estimates from the mean, is reduced by as much as 63%. / Doctor of Philosophy / On unmanned aerial systems (UASs), determining wind conditions indirectly, without direct measurements, is possible by utilizing onboard sensors and computational models. Often, the goal is to isolate the average wind speed while ignoring turbulent fluctuations. Conventionally, this is achieved using a mathematical tool called the KALMAN filter, which relies on assumptions about the wind. This dissertation introduces a novel approach called H-infinity (H∞) filtering, which does not rely on such assumptions and includes an additional mechanism to focus on specific wind frequencies of interest. The effectiveness of this method is evaluated using real-world data from UAS flights, comparing it with the traditional KALMAN-BUCY filter. Results show that the H∞ filter provides significantly improved wind estimates, with approximately 10-40% less error in most cases. Furthermore, the dissertation addresses the challenge of dealing with uncertainty in wind estimation. It introduces another mathematical technique called generalized polynomial chaos (gPC), which is used to quantify and manage uncertainties within the UAS system and their impact on the indirect wind estimates. By applying gPC, the dissertation shows that the amount and sources of uncertainty can be determined more efficiently than by traditional methods (up to 98% faster). Lastly, this dissertation shows the use of gPC to provide more precise wind estimates. In experimental scenarios, employing gPC in conjunction with H∞ filtering demonstrates superior performance compared to using a standard H∞ filter alone, reducing errors caused by uncertainty by as much as 63%.

This paper deals with the modeling of asynchronous motor. Ccomparing the simulation date to reality. And attempts to find an algorithm for calculating the mechanical torque assist state estimation engine.

Ce travail de thèse traite de la problématique d’estimation des défauts et de l’étathybride pour une classe de systèmes linéaires à commutations. L’objectif est de développerune méthode afin de synthétiser un observateur et un estimateur dédiésrespectivement à l’estimation de l’état hybride et des défauts. Après la présentationd’un état de l’art sur les techniques d’estimation, de stabilité et de diagnosticpour les systèmes linéaires à commutations, la thèse est scindée en deux parties.La première partie propose une méthode d’estimation de l’état continu et desdéfauts dans le cas où l’état discret du système est connu. En se basant sur unetransformation de coordonnées qui découple un sous-ensemble de l’état du systèmedes défauts, nous avons synthétisé dans un premier temps un observateur hybridepour estimer l’état continu du système, et dans un second temps, un estimateurpermettant la reconstruction des défauts. L’estimateur de défauts proposé dépendde la dérivée de la sortie du système. Pour cette raison, un différenciateur robusteet exact basé sur des techniques des modes glissants est utilisé. Dans la secondepartie de ce mémoire, l’état discret du système est supposé inconnu. Une approchebasée sur des méthodes algébriques est proposée afin d’estimer les instants decommutation entre les différents sous-systèmes. Par la suite, l’estimation de l’étathybride (état continu et état discret) et des défauts est considérée dans le cas oùl’état discret du système est inconnu. Ce dernier est reconstruit en se basant surles instant de commutation estimé et sur une séquence de commutation connue.L’état continu du système est estimé en se basant sur une méthode de placementde pôles permettant d’améliorer les performances de la phase transitoire. Enfin, enexploitant des résultats trouvés dans la première partie, l’estimation des défautsest considérée en estimant la sortie du système avec un différenciateur algébrique.Ce différenciateur donne des résultats plus intéressants vis-à-vis du bruit par rapportau différenciateur basé sur les techniques des modes glissants utilisé dans lapremière partie. / This work deals with the problem of estimation of fault and hybrid state for a classof switched linear systems. The objective is to develop a method to synthesize anobserver and an estimator dedicated respectively to the estimation of the hybridstate and the faults. After presenting a state of the art for estimation, stabilityand diagnostic techniques for switched linear systems, the report is divided intotwo parts. The first part proposes a method for estimating the continuous stateand the faults in the case where the discrete state of the system is known. Basedon a coordinate transformation which decouples a subset of the state of the systemof faults, we first synthesized a hybrid observer to estimate the continuous stateof the system and, in a second step, an estimator allowing the reconstructionof faults. The proposed fault estimator depends on the derivative of the systemoutput. For this reason, a robust and accurate differentiator based on sliding modetechniques is used. In the second part of this paper, the discrete state of the systemis assumed unknown. An algebraic approach is proposed to estimate the switchingtimes between the different subsystems. Thereafter, the estimation of the hybridstate (continuous and discrete state) and of the faults is considered in the casewhere the discrete state of the system is unknown. The latter is reconstructedfrom the estimated switching times and on a known switching sequence. Thecontinuous state of the system is estimated using a pole placement method allowingimprove the performances of the transient phase. Finally, by exploiting the resultsfound in the first part, the estimation of the faults is considered by estimatingthe output of the system with an algebraic differentiator. This differentiator givesmore interesting results at the noise compared to the differentiator based on thesliding mode techniques used in the first part.

Este trabalho tem como objetivo o desenvolvimento de uma metodologia de monitoramento a partir da alocação de medidores voltada para a estimação de estado harmônico trifásica em sistemas de distribuição de energia elétrica desequilibrados. O algoritmo de estimação de estado harmônico desenvolvido tem como entrada os fasores de tensão e de corrente em pontos pré-definidos de medição sobre os alimentadores em análise. Para a alocação dos medidores, verificou-se a necessidade de a mesma ser realizada e direcionada para este problema, e um algoritmo de otimização em específico foi desenvolvido utilizando algoritmos genéticos. Para a estimação de estado harmônico, a técnica de Decomposição em Valores Singulares foi utilizada, por ser adequada a sistemas não completamente observáveis. Em relação às simulações, cargas não lineares (ou perturbadoras) foram conectadas ao longo dos alimentadores testes do IEEE de 13, 34 e 37 barras, considerando configuração trifásica assimétrica para as linhas e cargas desbalanceadas. Todas as simulações computacionais foram realizadas dispondo do programa DIgSILENT PowerFactory. Os resultados satisfatórios encontrados denotam que o desempenho do estimador desenvolvido é dependente dos pontos de medição pré-definidos a partir da alocação dos medidores realizada. Pela metodologia implementada e aplicada, o algoritmo de estimação de estado harmônico veio a corretamente calcular todas as variáveis de estado e, consequentemente, os sistemas testes em análise tornaram-se completamente observáveis para todas as fases e ordens harmônicas caracterizadas. / This research aims for the development of a monitoring methodology through the allo-cation of meters in order to perform a three-phase harmonic state estimation in unbalanced distribution systems. The harmonic state estimation algorithm developed has voltage and current phasors as inputs at predefined measurement points on the feeders about analysis. For an allocation of the meters, there was a need for it to be performed and directed to this problem, and a specific optimization algorithm was developed using Genetic Algorithms. For a harmonic state estimation, the Singular Value Decomposition technique was made, because it is suitable for systems that are not completely observable. Regarding the simulations, the non-linear (or disturbing) loads were connected along the test feeders of the IEEE of 13, 34 and 37 bus, considering the three-phase asymmetric configuration for lines and loads. All computational simulations were performed in the DIgSILENT PowerFactory software. The satisfactory results found note that the performance of the developed estimator depends on the pre-defined measurement points from the allocation of the realized meters. By the applied methodology, the harmonic state estimation algorithm came to correctly calculate all the state variables and, consequently, the test systems about analysis became fully observable for all phases and harmonic orders characterized.

Neste trabalho propõe-se uma metodologia off-line, prática e eficiente, para detectar, identificar e corrigir erros em parâmetros série e shunt de linhas de transmissão. As linhas de transmissão, ou ramos do modelo barra-ramo, suspeitas de estarem com EPs são identificadas através do Índice de Suspeita (IS). O IS de um ramo é a relação entre o número de medidas incidentes a esse ramo, cujos resíduos normalizados são maiores que um valor pré-estabelecido, e o número total de medidas incidentes a esse ramo. Usando várias amostras de medidas, os parâmetros dos ramos suspeitos são estimados, de forma seqüencial, via um estimador de estado e parâmetros baseado nas equações normais, que aumenta o vetor de variáveis de estado para inclusão dos parâmetros suspeitos. Resultados numéricos de diversas simulações, com os sistemas de 14, 30 e 57 barras do IEEE, têm demonstrado a alta precisão e confiabilidade da metodologia proposta, mesmo na ocorrência de erros múltiplos (em mais de um parâmetro) em ramos adjacentes, como também em linhas de transmissão paralelas com compensação série. Comprovou-se a viabilidade prática da metodologia proposta através da aplicação da mesma, para depuração (detecção, identificação e correção) dos valores dos parâmetros de dois subsistemas da Hydro-Québec Trans-Énergie. / A practical and efficient off-line approach to detect, identify and correct series and shunt branch parameter errors is proposed in this thesis. The branches suspected of having parameter errors are identified by means of the Suspicious Index (SI). The SI of a branch is the ratio between the number of measurements incident to that branch, whose normalized residuals are larger than one specified threshold value, and the total number of measurements incident to that branch. Using several measurement snapshots, the suspicious parameters are sequentially estimated, via an augmented state and parameter estimator which increases the V-\'teta\' state vector for the inclusion of suspicious parameters. Several simulation results (with IEEE 14, 30 and 57 bus systems) have demonstrated the high accuracy and reliability of the proposed approach to de al with single and multiple parameter errors in adjacent and non-adjacent branches, as well as in parallel transmission lines with series compensation. The proposed approach is confirmed by tests performed in two subsystems of the Hydro-Québec Trans-Énergie.

Esta dissertação apresenta um estudo dos filtros de Kalman para sistemas singulares em tempo discreto. Novos algoritmos são formulados para as estimativas filtradas, preditoras e suavizadas com as correspondentes equações de Riccati para sistemas singulares variantes no tempo. Nesta dissertação considera-se também uma aproximação do problema de filtragem de Kalman como um problema determinístico de ajuste ótimo de trajetória. A formulação proposta permite considerar um atraso no sinal de medida, sendo permitida a correlação entre os estados e os ruídos da medida. Apresentam-se também as provas da estabilidade e da convergência destes filtros. / This dissertation presents a study of Kalman filters for singular systems in discrete time. New algorithms are developed for the Kalman filtered, predicted and smoothed estimate recursions with the corresponding Riccati equations for time-variant singular systems. This dissertation addresses the Kalman filtering problem as a deterministic optimal trajectory fitting problem. The problem is formulated taking into account one delay in the measured signals and correlations between state and measurement noises. In the final, this work presents the stability and convergence proofs of these filters.