A new network model has been developed which allows the calculation of state estimates for unbalanced electric power systems. This model incorporates the effects of mutually coupled conductors, earth return paths, unbalanced device configurations, and multiple voltage references. Development of the new model appeals to multiport network theory and graph theoretic principles. Model equations are employed directly to obtain least squares estimators in the phase-voltage reference frame. The concept of power system segments is introduced and segment multiport equations are developed. The concept of power system modified primitive networks is introduced and system multiport equations are developed. Segment and system multiport equations are used to obtain a state estimator formulation in variables suitable for practical systems analysis. / Ph. D.

La centralisations du travail, la croissance économique et celle de la population autant que l’urbanisation continue sont les causes principales de la congestion. Lors que les villes s’efforcent pour mettre à jour leurs infrastructures du trafic, l’utilisation de nouvelles techniques pour la modélisation, l’analyse de ces systèmes ainsi que l’intégration des mega données aux algorithmes aident à mieux comprendre et combattre les congestions, un aspect crucial pour le bon développement de nos villes intelligentes du XXIe siècle. Les outilsd’assistance de trafic spécialement conçus pour détecter, prévoir et alerter des conditions particulières sont très demandés dans nos jours.Cette recherche est consacrée au développement des algorithmes pour l’estimation et la prédiction sur des réseaux de trafic routier. Tout d’abord, nous considérons le problème de prévision à court terme du temps de trajet dynamique basé sur des méthodes pilotées par les données. Nous proposons deux techniques de fusion pour calculer les prévisions à court terme. Dans un première temps, nous considérons la matrice de covariance d’erreur et nous utilisons ses informations pour fusionner les prévisions individuelles créées á partir de clusters. Dans un deuxième temps, nous exploitons les mesures de similarité parmi le signal á prédire et des clusters dans l’histoire et on propose une fusion en tant que moyenne pondérée des sorties des prédicteurs de chaque cluster. Les résultats des deux méthodes on été validés dans le Grenoble Traffic Lab, un outil en temps réel qui permet la récupération de données d’une autoroute d’environ (10.5Km) qui couvre le sud de Grenoble.Postérieurement nous considérons le problème de reconstruction de la densité / et le débit de façon simultanée à partir de sources d’information hétérogènes. Le réseau de trafic est modélisé dans le cadre de modèles de trafic macroscopique, où nous adoptons l’équation de conservation Lighthill-Whitham-Richards avec un diagramme fondamental linaire par morceaux. Le problème d’estimation repose sur deux principes clés. Dans un premier temps, nous considérons la minimisation des erreurs entre les débits et les densités mesurés et reconstruits. Finalement, nous considérons l’état d’équilibre du réseau qui établit la loi de propagation des flux entrants et sortants dans le réseau. Tous les principes sont intégrés et le problème est présenté comme une optimisation quadratique avec des contraintes d’égalité a fin de réduire l’espace de solution des variables à estimer. Des scénarios de simulation basés sur des données synthétiques pour un réseau de manhattan sont fournis avec l’objectif de valider les performances de l’algorithme proposé. / Centralization of work, population and economic growth alongside continued urbanization are the main causes of congestion. As cities strive to update or expand aging infrastructure, the application of big data, new models and analytics to better understand and help to combat traffic congestion is crucial to the health and development of our smart cities of XXI century. Traffic support tools specifically designed to detect, forecast and alert these conditions are highly requested nowadays.This dissertation is dedicated to study techniques that may help to estimate and forecast conditions about a traffic network. First, we consider the problem Dynamic Travel Time (DTT) short-term forecast based on data driven methods. We propose two fusion techniques to compute short-term forecasts from clustered time series. The first technique considers the error covariance matrix and uses its information to fuse individual forecasts based on best linear unbiased estimation principles. The second technique exploits similarity measurements between the signal to be predicted and clusters detected in historical data and it performs afusion as a weighted average of individual forecasts. Tests over real data were implemented in the study case of the Grenoble South Ring, it comprises a highway of 10.5Km monitored through the Grenoble Traffic Lab (GTL) a real time application was implemented and open to the public.Based on the previous study we consider then the problem of simultaneous density/flow reconstruction in urban networks based on heterogeneous sources of information. The traffic network is modeled within the framework of macroscopic traffic models, where we adopt Lighthill-Whitham-Richards (LWR) conservation equation and a piecewise linear fundamental diagram. The estimation problem considers two key principles. First, the error minimization between the measured and reconstructed flows and densities, and second the equilibrium state of the network which establishes flow propagation within the network. Both principles are integrated together with the traffic model constraints established by the supply/demand paradigm. Finally the problem is casted as a constrained quadratic optimization with equality constraints in order to shrink the feasible region of estimated variables. Some simulation scenarios based on synthetic data for a manhattan grid network are provided in order to validate the performance of the proposed algorithm.

A naive approach to filtering for feedback control of dynamic systems that is robust and stable is proposed. Simulations are run on the filters presented to investigate the robustness properties of each filter. Each simulation with the comparison of the filters is carried out using the usual mean squared error. The filters to be included are the classic Kalman filter, Krein space Kalman, two adjustments to the Krein filter with input modeling and a second uncertainty parameter, a newly developed filter called the Naive filter, bias corrected Naive, exponentially weighted moving average (EWMA) Naive, and bias corrected EWMA Naive filter.

<p> Motivated by the increasing need to monitor safety-critical systems subject to uncer-<br> tainties, a novel set-membership approach is proposed to estimate the state of a dynamical<br> system with unknown-but-bounded exogenous inputs. By fully utilizing the system struc-<br> tural information, the proposed algorithm can address both computational efficiency and<br> estimation accuracy without requiring restrictive conditions on the system. Particularly,<br> the system is first decomposed into the strongly observable subsystem and the weakly un-<br> observable subsystem. To make full use of the subsystem’s properties, a set-membership<br> observer based on the unknown input observer and an ellipsoidal set-membership observer<br> are designed for the two subsystems, respectively. Then, the resulting set estimates from<br> each subsystem are fused and transformed to obtain the set estimate for the original system,<br> which is guaranteed to bound the actual system state. The conditions for the boundedness<br> of the proposed set estimate are discussed, and the proposed set-membership observer is also<br> tested numerically using illustrative examples.</p>

Esta tese investiga novas estratégias para a construção de modelos em tempo real de Sistemas Elétricos de Potência. Busca-se a melhoria das funções de Estimação de Estado e aplicações correlatas por meio da consideração da medição fasorial sincronizada, fornecida por dispositivos PMUs, em ambientes onde as regiões monitoradas são de domínios de empresas diferentes e cuja distribuição geográfica apresenta distâncias consideráveis, como é o caso brasileiro. Uma das tarefas mais críticas dentro deste contexto é a representação adequada de sistemas não monitorados, que devem ser modelados de forma precisa, robusta e, preferencialmente, considerando dados que são acessíveis ao operador. A incorporação de redes externas em estimação multiárea é efetuada por uma etapa adicional de estimação ou embutida diretamente nos processos iterativos locais, mediante, neste último caso, a exigência de contínuos fluxos de dados entre áreas. No entanto, constata-se, neste estudo, que modelos clássicos de Equivalentes Externos reduzidos, particularmente os modelos tipo Ward, atendem satisfatoriamente aos requisitos computacionais e de precisão do problema, desde que sejam devidamente atualizados a cada mudança do ponto de operação. Desta forma, considerando sincrofasores de tensão e de corrente coletados por PMUs em regiões de fronteira, desenvolve-se um modelo de Estimação de Estado Descentralizada em que a etapa de pós-processamento por agentes externos independentes é removida, permitindo a obtenção do estado interconectado em um único passo, sem intercâmbio de dados operacionais em tempo real. Dois modelos são implementados, que diferem essencialmente na forma de tratamento dos dados de equivalentes externos. A metodologia é codificada em linguagem C++, sendo validada nos Sistemas IEEE de 14, 30 e 118 barras sob várias configurações de medição e de particionamento, mediante análise estatística e comparação de estimativas com valores de referência. Os resultados obtidos indicam a viabilidade da proposta para o fornecimento de modelos de estimação de estado mais confiáveis, adaptados à atual tendência de descentralização de redes elétricas, sem grandes alterações nas funções já existentes e sob um custo computacional reduzido. Sugerem também a factibilidade do tratamento conjunto das funções relacionadas a Estimação de Estado e Equivalentes Externos. / New approaches for the real time modelling of Power Systems are investigated in this work. The improvement of State Estimation and related functions is pursued with the aid of synchronized measurements gathered by PMU devices, in a multi-owner environment where utilities are independent and distributed across large distances, as in the Brazilian interconnected system case. One of the critical tasks on this subject is the correct representation of non-monitored networks in precise and feasible way, where less data traffic between operators is preferable. In Multiarea State Estimation, the incorporation of external networks is usually performed as the additional estimation phase or directly included in local estimation models by means of inter-area communication channels. This research shows that classic models of External Equivalents, specially Ward types, meet the computational and precision requirements of the problem if they are correctly updated after changes in the operating point. Thus, by using voltage and current synchrophasors measured by boundary PMUs, a Decentralized State Estimation model is developed, where the need for a post-processing higher coordination step is suppressed, allowing the interconnected state to be found rapidly, in a single step and with no real time data exchange. Two strategies of including on-line information about External Equivalents are proposed, taking it as regular measurements or constraints to be imposed in the classical formulation. A computational software coded in C++ language is built to support the models, which are validated with the IEEE-14, 30 and 118 test bed systems, under several placement strategies and split network schemes. A consistent statistical analysis of the results is also performed, where outcomes are compared with reference values of a regular estimator. Results indicate the feasibility to generate reliable and robust real time models, without significant changes in existing energy management applications, and also shows the greater benefits of integrating State Estimation and External Equivalents into a single framework.

Esta tese investiga novas estratégias para a construção de modelos em tempo real de Sistemas Elétricos de Potência. Busca-se a melhoria das funções de Estimação de Estado e aplicações correlatas por meio da consideração da medição fasorial sincronizada, fornecida por dispositivos PMUs, em ambientes onde as regiões monitoradas são de domínios de empresas diferentes e cuja distribuição geográfica apresenta distâncias consideráveis, como é o caso brasileiro. Uma das tarefas mais críticas dentro deste contexto é a representação adequada de sistemas não monitorados, que devem ser modelados de forma precisa, robusta e, preferencialmente, considerando dados que são acessíveis ao operador. A incorporação de redes externas em estimação multiárea é efetuada por uma etapa adicional de estimação ou embutida diretamente nos processos iterativos locais, mediante, neste último caso, a exigência de contínuos fluxos de dados entre áreas. No entanto, constata-se, neste estudo, que modelos clássicos de Equivalentes Externos reduzidos, particularmente os modelos tipo Ward, atendem satisfatoriamente aos requisitos computacionais e de precisão do problema, desde que sejam devidamente atualizados a cada mudança do ponto de operação. Desta forma, considerando sincrofasores de tensão e de corrente coletados por PMUs em regiões de fronteira, desenvolve-se um modelo de Estimação de Estado Descentralizada em que a etapa de pós-processamento por agentes externos independentes é removida, permitindo a obtenção do estado interconectado em um único passo, sem intercâmbio de dados operacionais em tempo real. Dois modelos são implementados, que diferem essencialmente na forma de tratamento dos dados de equivalentes externos. A metodologia é codificada em linguagem C++, sendo validada nos Sistemas IEEE de 14, 30 e 118 barras sob várias configurações de medição e de particionamento, mediante análise estatística e comparação de estimativas com valores de referência. Os resultados obtidos indicam a viabilidade da proposta para o fornecimento de modelos de estimação de estado mais confiáveis, adaptados à atual tendência de descentralização de redes elétricas, sem grandes alterações nas funções já existentes e sob um custo computacional reduzido. Sugerem também a factibilidade do tratamento conjunto das funções relacionadas a Estimação de Estado e Equivalentes Externos. / New approaches for the real time modelling of Power Systems are investigated in this work. The improvement of State Estimation and related functions is pursued with the aid of synchronized measurements gathered by PMU devices, in a multi-owner environment where utilities are independent and distributed across large distances, as in the Brazilian interconnected system case. One of the critical tasks on this subject is the correct representation of non-monitored networks in precise and feasible way, where less data traffic between operators is preferable. In Multiarea State Estimation, the incorporation of external networks is usually performed as the additional estimation phase or directly included in local estimation models by means of inter-area communication channels. This research shows that classic models of External Equivalents, specially Ward types, meet the computational and precision requirements of the problem if they are correctly updated after changes in the operating point. Thus, by using voltage and current synchrophasors measured by boundary PMUs, a Decentralized State Estimation model is developed, where the need for a post-processing higher coordination step is suppressed, allowing the interconnected state to be found rapidly, in a single step and with no real time data exchange. Two strategies of including on-line information about External Equivalents are proposed, taking it as regular measurements or constraints to be imposed in the classical formulation. A computational software coded in C++ language is built to support the models, which are validated with the IEEE-14, 30 and 118 test bed systems, under several placement strategies and split network schemes. A consistent statistical analysis of the results is also performed, where outcomes are compared with reference values of a regular estimator. Results indicate the feasibility to generate reliable and robust real time models, without significant changes in existing energy management applications, and also shows the greater benefits of integrating State Estimation and External Equivalents into a single framework.

The enhancement of the reliability, security, and resiliency of electric power systems depends on the availability of fast, accurate, and robust dynamic state estimators. These estimators should be robust to gross errors on the measurements and the model parameter values while providing good state estimates even in the presence of large dynamical system model uncertainties and non-Gaussian thick-tailed process and observation noises. It turns out that the current Kalman filter-based dynamic state estimators given in the literature suffer from several important shortcomings, precluding them from being adopted by power utilities for practical applications. To be specific, they cannot handle (i) dynamic model uncertainty and parameter errors; (ii) non-Gaussian process and observation noise of the system nonlinear dynamic models; (iii) three types of outliers; and (iv) all types of cyber attacks. The three types of outliers, including observation, innovation, and structural outliers are caused by either an unreliable dynamical model or real-time synchrophasor measurements with data quality issues, which are commonly seen in the power system. To address these challenges, we have pioneered a general theoretical framework that advances both robust statistics and robust control theory for robust dynamic state and parameter estimation of a cyber-physical system. Specifically, the generalized maximum-likelihood-type (GM)-estimator, the unscented Kalman filter (UKF), and the H-infinity filter are integrated into a unified framework to yield various centralized and decentralized robust dynamic state estimators. These new estimators include the GM-iterated extended Kalman filter (GM-IEKF), the GM-UKF, the H-infinity UKF and the robust H-infinity UKF. The GM-IEKF is able to handle observation and innovation outliers but its statistical efficiency is low in the presence of non-Gaussian system process and measurement noise. The GM-UKF addresses this issue and achieves a high statistical efficiency under a broad range of non-Gaussian process and observation noise while maintaining the robustness to observation and innovation outliers. A reformulation of the GM-UKF with multiple hypothesis testing further enables it to handle structural outliers. However, the GM-UKF may yield biased state estimates in presence of large system uncertainties. To this end, the H-infinity UKF that relies on robust control theory is proposed. It is shown that H-infinity is able to bound the system uncertainties but lacks of robustness to outliers and non-Gaussian noise. Finally, the robust H-infinity filter framework is proposed that leverages the H-infinity criterion to bound system uncertainties while relying on the robustness of GM-estimator to filter out non-Gaussian noise and suppress outliers. Furthermore, these new robust estimators are applied for system bus frequency monitoring and control and synchronous generator model parameter calibration. Case studies of several different IEEE standard systems show the efficiency and robustness of the proposed estimators. / Ph. D. / The enhancement of the reliability, security, and resiliency of electric power systems depends on the availability of fast, accurate, and robust dynamic state estimators. These estimators should be robust to gross errors on the measurements and the model parameter values while providing good state estimates even in the presence of large dynamical system model uncertainties and non-Gaussian thick-tailed process and observation noises. There are three types of gross errors or outliers, namely, observation, innovation, and structural outliers. They can be caused by either an unreliable dynamical model or real-time synchrophasor measurements with data quality issues, which are commonly seen in the power system. The system uncertainties can be induced in several ways, including i) unknowable system inputs, such as noise, parameter variations and actuator failures, to name a few; ii) unavailable inputs, such as unmeasured mechanical power, field voltage of the exciter, unknown fault location; and iii) inaccuracies of the model parameter values of the synchronous generators, the loads, the lines, and the transformers, to name a few. It turns out that the current Kalman filter-based dynamic state estimators suffer from several important shortcomings, precluding them from being adopted by power utilities for practical applications. To address these challenges, this dissertation has proposed a general theoretical framework that advances both robust statistics and robust control theory for robust dynamic state and parameter estimation. Specifically, the robust generalized maximum-likelihood-type (GM)- estimator, the nonlinear filter, i.e., unscented Kalman filter (UKF), and the H-infinity filter are integrated into a unified framework to produce various robust dynamic state estimators. These new estimators include the robust GM-IEKF, the robust GM-UKF, the H-infinity UKF and the robust H-infinity UKF. Specifically, the GM-IEKF deals with the observation and innovation outliers but achieving relatively low statistical efficiency in the presence of non-Gaussian system process and measurement noise. To address that, the robust GM-UKF is proposed that is able to achieve a high statistical efficiency under a broad range of non-Gaussian noise while maintaining the robustness to observation and innovation outliers. A reformulation of the GM-UKF with multiple hypothesis testing further enables it to handle three types of outliers. However, the GM-UKF may yield biased state estimates in presence of large system uncertainties. To this end, the H-infinity UKF that depends on robust control theory is proposed. It is able to bound the system uncertainties but lacks of robustness to outliers and non-Gaussian noise. Finally, the robust H-infinity filter framework is proposed that relies on the H-infinity criterion to bound system uncertainties while leveraging the robustness of GM-UKF to filter out non-Gaussian noise and suppress outliers. These new robust estimators are applied for system bus frequency monitoring and control and synchronous generator model parameter calibration. Case studies of several different IEEE standard systems show the efficiency and robustness of the proposed estimators.

A review of aeroplane spin literature is presented, including early spin research history and lessons learned from spinning trials. Despite many years of experience in spinning evaluation, it is difficult to predict spin characteristics and problems have been encountered and several prototype aeroplanes have been lost. No currently published method will reliably predict an aeroplane’s spin recovery characteristics. Quantitative data is required to study the spin motion of the aeroplane in adequate detail. An alternative method, Vision Based State Estimation, has been used to capture the spin motion. This alternative method has produced unique illustrations of the spinning research aeroplane and data has been obtained that could possibly be very challenging to obtain using traditional methods. To investigate the aerodynamic flow of a spinning aeroplane, flights have been flown using wool tufts on wing, aft fuselage and empennage for flow visualization. To complement the tuft observations, the differential pressure between the upper and lower horizontal tail and wing surfaces have been measured at selected points. Tufts indicate that a large-scale Upper Surface Vortex forms on the outside wing. This USV has also been visualized using a smoke source. The flow structures on top of both wings, and on top of the horizontal tail surfaces, have also been studied on another aeroplane model. The development of these rotational flow effects has been related to the spin motion. It is hypothesized that the flow structure of the turbulent boundary layer on the outside upper wing surface is due to additional accelerations induced by the rotational motion of the aeroplane. The dynamic effects have been discussed and their importance for the development of the spin considered. In addition, it is suggested that another dynamic effect might exist due to the additional acceleration of the turbulent boundary layer due to the rotational motion of the aeroplane. It is recommended that future spin recovery prediction methods account for dynamic effects, in addition to aerodynamic control effectiveness and aeroplane inertia, since the spin entry phase is important for the subsequent development of the spin. Finally, suggestions for future research are given.

We study distributed estimation of dynamic random fields observed by a sparsely connected network of agents/sensors. The sensors are inexpensive, low power, and they communicate locally and perform computation tasks. In the era of large-scale systems and big data, distributed estimators, yielding robust and reliable field estimates, are capable of significantly reducing the large computation and communication load required by centralized estimators, by running local parallel inference algorithms. The distributed estimators have applications in estimation, for example, of temperature, rainfall or wind-speed over a large geographical area; dynamic states of a power grid; location of a group of cooperating vehicles; or beliefs in social networks. The thesis develops distributed estimators where each sensor reconstructs the estimate of the entire field. Since the local estimators have direct access to only local innovations, local observations or a local state, the agents need a consensus-type step to construct locally an estimate of their global versions. This is akin to what we refer to as distributed dynamic averaging. Dynamic averaged quantities, which we call pseudo-quantities, are then used by the distributed local estimators to yield at each sensor an estimate of the whole field. Using terminology from the literature, we refer to the distributed estimators presented in this thesis as Consensus+Innovations-type Kalman filters. We propose three distinct types of distributed estimators according to the quantity that is dynamically averaged: (1) Pseudo-Innovations Kalman Filter (PIKF), (2) Distributed Information Kalman Filter (DIKF), and (3) Consensus+Innovations Kalman Filter (CIKF). The thesis proves that under minimal assumptions the distributed estimators, PIKF, DIKF and CIKF converge to unbiased and bounded mean-squared error (MSE) distributed estimates of the field. These distributed algorithms exhibit a Network Tracking Capacity (NTC) behavior – the MSE is bounded if the degree of instability of the field dynamics is below a threshold. We derive the threshold for each of the filters. The thesis establishes trade-offs between these three distributed estimators. The NTC of the PIKF depends on the network connectivity only, while the NTC of the DIKF and of the CIKF depend also on the observation models. On the other hand, when all the three estimators converge, numerical simulations show that the DIKF improves 2dB over the PIKF. Since the DIKF uses scalar gains, it is simpler to implement than the CIKF. Of the three estimators, the CIKF provides the best MSE performance using optimized gain matrices, yielding an improvement of 3dB over the DIKF. Keywords: Kalman filter, distributed state estimation, multi-agent networks, sensor networks, distributed algorithms, consensus, innovation, asymptotic convergence, mean-squared error, dynamic averaging, Riccati equation, Lyapunov iterations, distributed signal processing, random dynamical systems.

In this dissertation, joint detection-state estimation and secure signal processing are studied. Detection and state estimation are two important research topics in surveillance systems. The detection problems investigated in this dissertation include object detection and fault detection. The goal of object detection is to determine the presence or absence of an object under measurement uncertainty. The aim of fault detection is to determine whether or not the measurements are provided by faulty sensors. State estimation is to estimate the states of moving objects from measurements with random measurement noise or disturbance, which typically consist of their positions and velocities over time. Detection and state estimation are typically implemented separately and state estimation is usually performed after the decision is made. In this two-stage approach, missed detection and false alarms in detection stage decrease accuracy of state estimation. In this dissertation, several joint detection and state estimation algorithms are proposed. Secure signal processing is indispensable in dynamic systems especially when an adversary exists. In this dissertation, the developed joint fault detection and state estimation approach is used to detect attacks launched by an adversary on the system and improve state estimation accuracy. The security problem in satellite communication systems is studied and a minimax anti-jammer is designed in a frequency hopping spread spectrum (FHSS)/quadrature phase-shift keying (QPSK) satellite communication system.