Recent widespread blackouts have indicated the need for more efficient and accurate power system monitoring, control and protection tools. Power system state estimation, which is the major tool that is used nowadays for providing the real-time model of the system, has significant biases resulting mainly from the complexity and geographic spread and separation of an electric power system. Synchrophasor technology is a promising technology that has numerous advantages compared to conventional metering devices. PMUs provide synchronized measurements, where synchronization is achieved via a GPS clock which provides the synchronizing signal with accuracy of 1 μsec. As a result, the computed phasors have a common reference (UTC time) and can be used in local computations, thus distributing the state estimation process. The first part of the work presents a PMU enabled dynamic state estimator (DSE) that can capture with high fidelity the dynamics of the system and extract in real time the dynamic model of the system. The described DSE is performed in a decentralized way, on the substation level based on local measurements which are globally valid. The substation based DSE uses data from relays, PMUs, meters, FDRs etc in the substation only, thus avoiding all issues associated with transmission of data and associated time latencies. This approach enables very fast DSE update rate which can go up to more than 60 executions per second. The distributed state estimation architecture that synchrophasor technology enables, along with the fast sampling rate and the accuracy of the measurements that PMUs provide, enable the computation of the real-time dynamic model of the system and the development of numerous power system applications for more efficient control and protection of the system. In the second part of the work, a transient stability monitoring scheme is presented that utilizes the information given by the dynamic state estimation and enables real-time monitoring of the transient swings of the system and characterizes the stability of the system in real time. In particular, the real-time dynamic model of the system, as given by the DSE, is utilized to evaluate the system's energy function based on Lyapunov's direct method and extract stability properties from the energy function. The two major components of the scheme are a) the calculation of the center of oscillations of the system and b) the derivation of an equivalent, reduced sized model which is used for the calculation of the potential and kinetic energy of the system based on which the stability of the system is determined. Finally, as an application of the transient stability monitoring scheme, an energy based out-of-step protection scheme is proposed. The energy of the generator is continuously monitored and if it exceeds a predefined threshold then instability is asserted and a trip signal can be sent to the generator. The major advantage of the scheme is that the out-of-step condition is predicted before its occurrence and therefore relays can act much faster than today's technology. The scheme is compared to presently available state of the art out-of-step protection schemes in order to verify its superiority.

This thesis presents different state estimation techniques for speed sensorlees field oriented control of induction motors. The theoretical basis of each algorithm is explained in detail and its performance is tested with simulations and experiments individually. First, a stochastical nonlinear state estimator, Extended Kalman Filter (EKF) is presented. The motor model designed for EKF application involves rotor speed, dq-axis rotor fluxes and dq-axis stator currents. Thus, using this observer the rotor speed and rotor fluxes are estimated simultaneously. Different from the widely accepted use of EKF, in which it is optimized for either steady-state or transient operations, here using adjustable noise level process algorithm the optimization of EKF has been done for both states / the steady-state and the transient-state of operations. Additionally, the measurement noise immunity of EKF is also investigated. Second, Unscented Kalman Filter (UKF), which is an updated version of EKF, is proposed as a state estimator for speed sensorless field oriented control of induction motors. UKF state update computations, different from EKF, are derivative free and they do not involve costly calculation of Jacobian matrices. Moreover, variance of each state is not assumed Gaussian, therefore a more realistic approach is provided by UKF. In this work, the superiority of UKF is shown in the state estimation of induction motor. Third, Model Reference Adaptive System is studied as a state estimator. Two different methods, back emf scheme and reactive power scheme, are applied to MRAS algorithm to estimate rotor speed. Finally, a flux estimator and an open-loop speed estimator combination is employed to observe stator-rotor fluxes, rotor-flux angle and rotor speed. In flux estimator, voltage model is assisted by current model via a closed-loop to compensate voltage model&rsquo / s disadvantages.

The purpose of this report is to evaluate different methods for identifying states in robot models. Both linear and non-linear filters exist among these methods and are compared to each other. Advantages, disadvantages and problems that can occur during tuning and running are presented. Additional measurements from accelerometers are added and their use with above mentioned methods for state estimation is evaluated. The evaluation of methods in this report is mainly based on simulations in Matlab, even though some experiments have been performed on laboratory equipment. The conclusion indicates that simple non-linear models with few states can be more accurately estimated with a Kalman filter than with an extended Kalman filter, as long as only linear measurements are used. When non-linear measurements are used an extended Kalman filteris more accurate than a Kalman filter. Non-linear measurements are introduced through accelerometers with non-linear measurement equations. Using accelerometers generally leads to better state estimation when the measure equations have a simple relation to the model.

Modeling and inferential control studies are carried out on a reactive batch distillation system for the esterification reaction of ethanol with acetic acid to produce ethyl acetate. A dynamic model is developed based on a previous study done on a batch distillation column. The column is modified for a reactive system where Artificial Neural Network Estimator is used instead of Extended Kalman Filter for the estimation of compositions of polar compounds for control purposes. The results of the developed dynamic model of the column is verified theoretically with the results of a similar study. Also, in order to check the model experimentally, a lab scale column (40 cm height, 5 cm inner diameter with 8 trays) is used and it is found that experimental data is not in good agreement with the models&rsquo / . Therefore, the model developed is improved by using different rate expressions and thermodynamic models (fi-fi, combination of equations of state (EOS) and excess Gibbs free energy (EOS-Gex), gama-fi) with different equations of states (Peng Robinson (PR) / Peng Robinson - Stryjek-Vera (PRSV)), mixing rules (van der Waals / Huron Vidal (HV) / Huron Vidal Original (HVO) / Orbey Sandler Modification of HVO (HVOS)) and activity coefficient models (NRTL / Wilson / UNIQUAC). The gama-fi method with PR-EOS together with van der Waals mixing rule and NRTL activity coefficient model is selected as the best relationships which fits the experimental data. The thermodynamic models / EOS, mixing rules and activity coefficient models, all are found to have very crucial roles in modeling studies. A nonlinear optimization problem is also carried out to find the optimal operation of the distillation column for an optimal reflux ratio profile where the maximization of the capacity factor is selected as the objective function. In control studies, to operate the distillation system with the optimal reflux ratio profile, a control system is designed with an Artificial Neural Network (ANN) Estimator which is used to predict the product composition values of the system from temperature measurements. The network used is an Elman network with two hidden layers. The performance of the designed network is tested first in open-loop and then in closed-loop in a feedback inferential control algorithm. It is found that, the control of the product compositions with the help of an ANN estimator with error refinement can be done considering optimal reflux ratio profile.

The focus of the present thesis is on the joint use of cameras and inertial sensors, a recent area of active research. Within our scope, the performance of body state estimation is investigated with isolated inertial sensors, isolated cameras and finally with a fusion of two types of sensors within a Kalman Filtering framework. The study consists of both simulation and real hardware experiments. The body state estimation problem is restricted to a single axis rotation where we estimate turn angle and turn rate. This experimental setup provides a simple but effective means of assessing the benefits of the fusion process. Additionally, a sensitivity analysis is carried out in our simulation experiments to explore the sensitivity of the estimation performance to varying levels of calibration errors. It is shown by experiments that state estimation is more robust to calibration errors when the sensors are used jointly. For the fusion of sensors, the Indirect Kalman Filter is considered as well as the Direct Form Kalman Filter. This comparative study allows us to assess the contribution of an accurate system dynamical model to the final state estimates. Our simulation and real hardware experiments effectively show that the fusion of the sensors eliminate the unbounded error growth characteristic of inertial sensors while final state estimation outperforms the use of cameras alone. Overall we can v demonstrate that the Kalman based fusion result in bounded error, high performance estimation of body state. The results are promising and suggest that these benefits can be extended to body state estimation for multiple degrees of freedom.

This thesis presents an all-inclusive framework on how the current formant tracking and audio (and/or visual)-to-articulatory inversion algorithms can be improved. The possible improvements are summarized as follows: The first part of the thesis investigates the problem of the formant frequency estimation when the number of formants to be estimated fixed or variable respectively. The fixed number of formant tracking method is based on the assumption that the number of formant frequencies is fixed along the speech utterance. The proposed algorithm is based on the combination of a dynamic programming algorithm and Kalman filtering/smoothing. In this method, the speech signal is divided into voiced and unvoiced segments, and the formant candidates are associated via dynamic programming algorithm for each voiced and unvoiced part separately. Individual adaptive Kalman filtering/smoothing is used to perform the formant frequency estimation. The performance of the proposed algorithm is compared with some algorithms given in the literature. The variable number of formant tracking method considers those formant frequencies which are visible in the spectrogram. Therefore, the number of formant frequencies is not fixed and they can change along the speech waveform. In that case, it is also necessary to estimate the number of formants to track. For this purpose, the proposed algorithm uses extra logic (formant track start/end decision unit). The measurement update of each individual formant trajectories is handled via Kalman filters. The performance of the proposed algorithm is illustrated by some examples The second part of this thesis is concerned with improving audiovisual to articulatory inversion performance. The related studies can be examined in two parts / Gaussian mixture model (GMM) regression based inversion and Jump Markov Linear System (JMLS) based inversion. GMM regression based inversion method involves modeling audio (and /or visual) and articulatory data as a joint Gaussian mixture model. The conditional expectation of this distribution gives the desired articulatory estimate. In this method, we examine the usefulness of the combination of various acoustic features and effectiveness of various types of fusion techniques in combination with audiovisual features. Also, we propose dynamic smoothing methods to smooth articulatory trajectories. The performance of the proposed algorithm is illustrated and compared with conventional algorithms. JMLS inversion involves tying the acoustic (and/or visual) spaces and articulatory space via multiple state space representations. In this way, the articulatory inversion problem is converted into the state estimation problem where the audiovisual data are considered as measurements and articulatory positions are state variables. The proposed inversion method first learns the parameter set of the state space model via an expectation maximization (EM) based algorithm and the state estimation is handled via interactive multiple model (IMM) filter/smoother.

In many systems, instabilities can lead to time-dependent behavior, and instabilities can act as mechanisms for sustained chaos; an understanding of the dynamical modes governing instability is thus essential for prediction and/or control in such systems. In this thesis work, we have developed an approach toward characterizing instabilities quantitatively, from experiments on the prototypical Rayleigh-Bénard convection system. We developed an experimental technique for preparing a given convection pattern using rapid optical actuation of pressurized SF6, a greenhouse gas. Real-time analysis of convection patterns was developed as part of the implementation of closed-loop control of straight roll patterns. Feedback control of the patterns via actuation was used to guide patterns to various system instabilities. Controlled, spatially localized perturbations were applied to the prepared states, which were observed to excite the dominant system modes. We extracted the spatial structure and growth rates of these modes from analysis of the pattern evolutions. The lifetimes of excitations were also measured, near a particular instability; a critical wavenumber was found from the observed dynamical slowing near the bifurcation. We will also describe preliminary results of using a state estimation algorithm (LETKF) on experimentally prepared non-periodic patterns in a cylindrical convection cell.

The contributions of the research are (a) an infrastructure of data acquisition systems that provides the necessary information for an automated EMS system enabling autonomous distributed state estimation, model validation, simplified protection, and seamless integration of other EMS applications, (b) an object-oriented, interoperable, and unified component model that can be seamlessly integrated with a variety of applications of the EMS, (c) a distributed dynamic state estimator (DDSE) based on the proposed data acquisition system and the object-oriented, interoperable, and unified component model, (d) a physically-based synchronous machine model, which is expressed in terms of the actual self and mutual inductances of the synchronous machine windings as a function of rotor position, for the purpose of synchronous machine parameters identification, and (e) a robust and highly efficient algorithm for the optimal power flow (OPF) problem, one of the most important applications of the EMS, based on the validated states and models of the power system provided by the proposed DDSE.

Realistic physical-biological ocean models pose challenges to statistical techniques due to their complexity, nonlinearity and high dimensionality. In this thesis, statistical data assimilation techniques for parameter and state estimation are adapted and applied to biological models. These methods rely on quantitative measures of agreement between models and observations. Eight such measures are compared and a suitable multiscale measure is selected for data assimilation. Build on this, two data assimilation approaches, a particle filter and a computationally efficient emulator approach are tested and contrasted. It is shown that both are suitable for state and parameter estimation. The emulator is also used to analyze sensitivity and uncertainty of a realistic biological model. Application of the statistical procedures yields insights into the model; e.g. time-dependent parameter estimates are obtained which are consistent with biological seasonal cycles and improves model predictions as evidenced by cross-validation experiments. Estimates of model sensitivity are high with respect to physical model inputs, e.g river runoff.

Nesta dissertação é apresentada a comparação do desempenho de três estimadores - o Filtro de Kalman Estendido, o Filtro de Kalman Unscented e o Filtro de Partículas - aplicados para estimar a postura de um robô diferencial. Uma câmera foi fixa no teto para cobrir todo o campo operacional do robô durante os experimentos, a fim de extrair o mapa e gerar o ground truth. Isso permitiu realizar uma análise do erro de forma precisa a cada instante de tempo. O desempenho de cada um dos estimadores foi avaliado sistematicamente e numericamente para duas trajetórias. Os resultados desse primeiro experimento demonstram que os filtros proporcionam grandes melhorias em relação à odometria e que o modelo dos sensores é crítico para obter esse desempenho. O Filtro de Partículas mostrou um desempenho melhor em relação aos demais nos dois percursos. No entanto, seu elevado custo computacional dificulta sua implementação em uma aplicação de tempo real. O Filtro de Kalman Unscented, por sua vez, mostrou um desempenho semelhante ao Filtro de Kalman Estendido durante a primeira trajetória. Porém, na segunda trajetória, a qual possui uma quantidade maior de curvas, o Filtro de Kalman Unscented mostrou uma melhora significativa em relação ao Filtro de Kalman Estendido. Foi realizado um segundo experimento, em que o robô planeja e executa duas trajetórias. Os resultados obtidos mostraram que o robô consegue chegar a um determinado local com uma precisão da mesma ordem de grandeza do que a obtida durante a estimação de estados do robô. / In this dissertation, the performance of three nonlinear-model based estimators - the Extended Kalman Filter, the Unscented Kalman Filter and the Particle Filter - applied to pose estimation of a differential drive robot is compared. A camera was placed above the operating field of the robot to record the experiments in order to extract the map and generate the ground truth so the evaluation of the error can be done at each time step with high accuracy. The performance of each estimator is assessed systematically and numerically for two robot trajectories. The first experimental results showed that all estimators provide large improvements with respect to odometry and that the sensor modeling is critical for their performance. The particle filter showed a better performance than the others on both experiments, however, its high computational cost makes it difficult to implement in a real-time application. The Unscented Kalman Filter showed a similar performance to the Extended Kalman Filter during the first trajectory. However, during the second one (a curvier path) the Unscented Kalman Filter showed a significant improvement over the Extended Kalman Filter. A second experiment was carried out where the robot plans and executes a trajectory. The results showed the robot can reach a predefined location with an accuracy of the same order of magnitude as the obtained during the robot pose estimation.