La modélisation des systèmes dynamiques requiert la prise en compte d’incertitudes liées à l’existence inévitable de bruits (bruits de mesure, bruits sur la dynamique), à la méconnaissance de certains phénomènes perturbateurs mais également aux incertitudes sur la valeur des paramètres (spécification de tolérances, phénomène de vieillissement). Alors que certaines de ces incertitudes se prêtent bien à une modélisation de type statistique comme par exemple ! les bruits de mesure, d’autres se caractérisent mieux pa ! r des bornes, sans autre attribut. Dans ce travail de thèse, motivés par les observations ci-dessus, nous traitons le problème de l’intégration d’incertitudes statistiques et à erreurs bornées pour les systèmes linéaires à temps discret. Partant du filtre de Kalman Intervalle (noté IKF) développé dans [Chen 1997], nous proposons des améliorations significatives basées sur des techniques récentes de propagation de contraintes et d’inversion ensembliste qui, contrairement aux mécanismes mis en jeu par l’IKF, permettent d’obtenir un résultat garanti tout en contrôlant le pessimisme de l’analyse par intervalles. Cet algorithme est noté iIKF. Le filtre iIKF a la même structure récursive que le filtre de Kalman classique et délivre un encadrement de tous les estimés optimaux et des matrices de covariance possibles. L’algorithme IKF précédent évite quant à lui le problème de l’inversion des matrices intervalles, ce qui lui vaut de perdre des solutions possibles. Pour l’iIKF, nous proposons une méthode originale garantie pour l’inversion des matrices intervalle qui couple l’algorithme SIVIA (Set Inversion via Interval Analysis) et un ensemble de problèmes de propagation de contraintes. Par ailleurs, plusieurs mécanismes basés sur la propagation de contraintes sont également mis en œuvre pour limiter l’effet de surestimation due à la propagation d’intervalles dans la structure récursive du filtre. Un algorithme de détection de défauts basé sur iIKF est proposé en mettant en œuvre une stratégie de boucle semi-fermée qui permet de ne pas réalimenter le filtre avec des mesures corrompues par le défaut dès que celui-ci est détecté. A travers différents exemples, les avantages du filtre iIKF sont exposés et l’efficacité de l’algorithme de détection de défauts est démontré. / In this thesis, a new approach to estimation problems under the presence of bounded uncertain parameters and statistical noise has been presented. The objective is to use the uncertainty model which appears as the most appropriate for every kind of uncertainty. This leads to the need to consider uncertain stochastic systems and to study how the two types of uncertainty combine : statistical noise is modeled as the centered gaussian variable and the unknown but bounded parameters are approximated by intervals. This results in an estimation problem that demands the development of mixed filters and a set-theoretic strategy. The attention is drawn on set inversion problems and constraint satisfaction problems. The former is the foundation of a method for solving interval equations, and the latter can significantly improve the speed of interval based arithmetic and algorithms. An important contribution of this work consists in proposing an interval matrix inversion method which couples the algorithm SIVIA with the construction of a list of constraint propagation problems. The system model is formalized as an uncertain stochastic system. Starting with the interval Kalman filtering algorithm proposed in [Chen 1997] and that we name the IKF, an improved interval Kalman filtering algorithm (iIKF) is proposed. This algorithm is based on interval conditional expectation for interval linear systems. The iIKF has the same structure as the conventional Kalman filter while achieving guaranteed statistical optimality. The recursive computational scheme is developed in the set-membership context. Our improvements achieve guaranteed interval inversion whereas the original version IKF [Chen 1997] uses an instance (the upper bound) of the interval matrix to avoid the possible singularity problems. This point of view leads to a sub-optimal solution that does not preserve guaranteed results, some solutions being lost. On the contrary, in the presence of unknown-but-bounded parameters and measurement statistical errors, our estimation approach in the form of the iIKF provides guaranteed estimates, while maintaining a computational burden comparable to that of classic statistical approaches. Several constraint based techniques have also been implemented to limit the overestimation effect due to interval propagation within the interval Kalman filter recursive structure. The results have shown that the iIKF out puts bounded estimates that enclose all the solutions consistent with bounded errors and achieves good overestimation control. iIKF is used to propose a fault detection algorithm which makes use of a Semi-Closed Loop strategy which does not correct the state estimate with the measure as soon as a fault is detected. Two methods for generating fault indicators are proposed : they use the a priori state estimate and a threshold based on the a posteriori and a priori covariance matrix, respectively, and check the consistency against the measured output. Through different examples, the advantages of the iIKF with respect to previous versions are exhibited and the efficiency of the iIKF based Semi-Closed Loop fault detection algorithm is clearly demonstrated.

Esta dissertação trata do problema de projeto e fortalecimento de sistemas de medição, para efeito de estimação de estado em sistemas elétricos de potência. São dois os objetivos principais desta dissertação. O primeiro é o desenvolvimento e implementação, em computador, de uma metodologia para projeto e fortalecimento de sistemas de medição confiáveis que fazem uso de medidas convencionais obtidas pelo sistema SCADA e de medidas fasoriais sincronizadas. Haja vista a existência de medidas redundantes que apresentam a característica de não refletirem grande parcela de seus erros nos resíduos do estimador por mínimos quadrados ponderados, definidas em (BENEDITO et al., 2013) como medidas com elevado índice UI, o segundo objetivo desta dissertação é o desenvolvimento e implantação, em computador, de uma metodologia para projeto e fortalecimento de sistemas de medição confiáveis que, além de considerar os critérios técnicos de confiabilidade para efeito de estimação de estado, considere também o índice UI das medidas. A metodologia possibilita a obtenção de sistemas de medição confiáveis formados por medidas com índice UI baixo. Para o desenvolvimento das metodologias propostas são utilizados como base algoritmo evolutivo monobjetivo e o método para projeto de sistemas de medição que faz uso da chamada matriz HΔT, que é obtida via um processo de fatoração triangular da matriz jacobiana transposta do estimador de estado por mínimos quadrados ponderados. / This thesis focuses on the problem of metering system planning for state estimation purposes and has two main objectives. The first one is to develop a methodology for metering system planning that allows the project of reliable metering systems considering both conventional and synchronized phasor measurements. Because of the existence of redundant measurements that have the characteristics of not reflecting their errors into the residuals of the weighted least squares estimator, called in (BENEDITO et al., 2013) as measurements with high Undetectability Index (UI), the second objective of this thesis is to develop a methodology, for metering system planning, that allow the project of reliable metering systems formed by measurements with UI lower than a pre-specified value. The background to develop the proposed methodologies are evolutionary algorithms and the method to metering system planning based on the analysis of the HΔt matrix, that is obtained from the triangular factorization of the transpose Jacobian matrix of the weighted least squares estimator.

Nos modernos centros de operação dos Sistemas Elétricos de Potência (SEP), as variáveis de estado estimadas, ao invés das medidas, constituem a base de dados para as ações de controle e operação em tempo real. Desta forma, o processo de estimação de estado é de fundamental importância para operação dos SEP. O sucesso do processo de estimação de estado depende do sistema de medição disponível, isto é, do número, tipo e localização dos medidores e das Unidades Terminais Remotas (UTRs), instalados no SEP. Desenvolveu-se, neste trabalho, uma metodologia para projeto e fortalecimento de sistemas de medição, para efeito de estimação de estado. A metodologia baseia-se em Algoritmos Evolutivos (AEs) e na estrutura da matriz H \'delta\'. Pela análise da estrutura dessa matriz, que é obtida via um processo de fatoração triangular da matriz Jacobiana, a metodologia desenvolvida possibilita a obtenção de sistemas de medição confiáveis (SMC), considerando a possibilidade de o sistema possuir diferentes topologias. Neste trabalho, um sistema de medição é considerado confiável se for observável e não possuir medidas críticas, conjunto crítico de medidas e UTRs críticas. Um AE foi desenvolvido para obtenção do melhor SMC, com custo mínimo de investimento. Essa abordagem utiliza uma função de fitness que mede o custo da instalação de medidores e UTRs para obtenção de um determinado SMC. Uma vantagem relevante da metodologia desenvolvida é a sua estratégia para a obtenção de SMCs. Uma codificação indireta do cromossomo, representando uma ordem preferencial de instalação de medidores, combinada com as propriedades da matriz H \'delta\', garante ao AE a geração somente de soluções viáveis, ou seja, SMCs. Para comprovar a eficiência da metodologia desenvolvida, vários testes foram realizados, utilizando os sistemas de 6, 14, 30 e 118 barras do IEEE, bem como o sistema de 61 barras da Eletropaulo. / In modern operating control centers, the estimated state variables, instead of the measured state variables, constitute the database used to set up power systems real-time control actions. Consequently, the state estimation process is essential for power system real-time operation. The success of the state estimation process depends on the available metering systems, that is, on the topological distribution of the established meters and Remote Terminal Units (RTUs) on the system. A methodology for metering system planning for state estimation purposes was developed in this work. The methodology is based on both Evolutionary Algorithms (EAs) and on the analysis of the called H \'delta\' matrix. By analyzing the structure of this matrix, which is obtained via a triangular factorization of the Jacobian matrix, the developed methodology can determine reliable metering systems (RMS), under many different topology scenarios. In this work a metering system is considered as reliable if it is observable and has no critical measurements, critical sets neither critical RTUs. An EA was developed to find the best RMS with minimal investment cost. The developed EA uses a fitness function that measures the installation cost of meters and RTUs from a given RMS. One relevant advantage of the developed methodology is its strategy to obtain RMS. An indirect chromosome encoding representing a preferential order of meters installation combined with properties of the H \'delta\' matrix guarantees the proposed EA generates only feasible solutions, i.e. RMSs. In order to validate the developed methodology, several tests were executed considering the IEEE 6, 14, 30 and 118 bus systems, as well as the real system with 61 buses from Eletropaulo.

This thesis develops an advanced battery monitoring and control system based on the electrochemical principles that govern lithium-ion battery dynamics. This work is motivated by the need of having safer and better energy storage systems for all kind of applications, from small scale portable electronics to large scale renewable energy storage. In this context, lithium-ion batteries have become the enabling technology for energy autonomy in appliances (e.g. mobile phone, electric vehicle) and energy self-consumption in households. However, batteries are oversized and pricey, might be unsafe, are slow to charge and may not equalize the lifetime of the application they are intended to power. This work tackles these different issues.This document first introduces the general context of the battery management problem, as well as the particular issues that arise when modeling, supervising and controlling the battery short-term and long-term operation. Different solutions coming from the literature are reviewed, and several standard tools borrowed from control theory are exposed. Then, starting by well-known contributions in electrochemical modeling, we proceed to develop reduced-order models for the battery operation including degradation mechanisms, that are highly descriptive of the real phenomena taking place. This modeling framework is the cornerstone of all the monitoring and control development that follows.Next, we derive a battery diagnosis system with a twofold objective. First, indicators for internal faults affecting the battery state-of-health are obtained. Secondly, detection and isolation of sensor faults is achieved. Both tasks rely on state observers designed from electrochemical models to perform state estimation and residual generation. Whereas the former solution resorts to system identification techniques for health monitoring, the latter solution exploits fault diagnosis for instrumentation assessment.We then develop a feedback battery charge strategy able to push in performance while accounting for constraints associated to battery degradation. The fast and safe charging capabilities of the proposed approach are ultimately validated through long-term cycling experiments. This approach outperforms widely used commercial charging strategies in terms of both charging speed and degradation.The main contribution of this thesis is the exploitation of first principles models to develop battery management strategies towards improving safety, charging time and lifetime of battery systems without jeopardizing performance. The obtained results show that system and control theory offer opportunities to improve battery operation, aside from the material sciences contributions to this field. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

Esta dissertação trata do problema de projeto e fortalecimento de sistemas de medição, para efeito de estimação de estado em sistemas elétricos de potência. São dois os objetivos principais desta dissertação. O primeiro é o desenvolvimento e implementação, em computador, de uma metodologia para projeto e fortalecimento de sistemas de medição confiáveis que fazem uso de medidas convencionais obtidas pelo sistema SCADA e de medidas fasoriais sincronizadas. Haja vista a existência de medidas redundantes que apresentam a característica de não refletirem grande parcela de seus erros nos resíduos do estimador por mínimos quadrados ponderados, definidas em (BENEDITO et al., 2013) como medidas com elevado índice UI, o segundo objetivo desta dissertação é o desenvolvimento e implantação, em computador, de uma metodologia para projeto e fortalecimento de sistemas de medição confiáveis que, além de considerar os critérios técnicos de confiabilidade para efeito de estimação de estado, considere também o índice UI das medidas. A metodologia possibilita a obtenção de sistemas de medição confiáveis formados por medidas com índice UI baixo. Para o desenvolvimento das metodologias propostas são utilizados como base algoritmo evolutivo monobjetivo e o método para projeto de sistemas de medição que faz uso da chamada matriz HΔT, que é obtida via um processo de fatoração triangular da matriz jacobiana transposta do estimador de estado por mínimos quadrados ponderados. / This thesis focuses on the problem of metering system planning for state estimation purposes and has two main objectives. The first one is to develop a methodology for metering system planning that allows the project of reliable metering systems considering both conventional and synchronized phasor measurements. Because of the existence of redundant measurements that have the characteristics of not reflecting their errors into the residuals of the weighted least squares estimator, called in (BENEDITO et al., 2013) as measurements with high Undetectability Index (UI), the second objective of this thesis is to develop a methodology, for metering system planning, that allow the project of reliable metering systems formed by measurements with UI lower than a pre-specified value. The background to develop the proposed methodologies are evolutionary algorithms and the method to metering system planning based on the analysis of the HΔt matrix, that is obtained from the triangular factorization of the transpose Jacobian matrix of the weighted least squares estimator.

This dissertation develops a new concept for performance-based monitoring (PBM) of instrumented buildings subjected to earthquakes. This concept is achieved by simultaneously combining and advancing existing knowledge from structural mechanics, signal processing, and performance-based earthquake engineering paradigms. The PBM concept consists of 1) optimal sensor placement, 2) dynamic response reconstruction, 3) damage estimation, and 4) loss analysis. Within the proposed concept, the main theoretical contribution is the derivation of a nonlinear model-based observer (NMBO) for state estimation in nonlinear structural systems. The NMBO employs an efficient iterative algorithm to combine a nonlinear model and limited noise-contaminated response measurements to estimate the complete nonlinear dynamic response of the structural system of interest, in the particular case of this research, a building subject to an earthquake. The main advantage of the proposed observer over existing nonlinear recursive state estimators is that it is specifically designed to be physically realizable as a nonlinear structural model. This results in many desirable properties, such as improved stability and efficiency. Additionally, a practical methodology is presented to implement the proposed PBM concept in the case of instrumented steel, wood-frame, and reinforced concrete buildings as the three main types of structural systems used for construction in the United States. The proposed methodology is validated using three case studies of experimental and real-world large-scale instrumented buildings. The first case study is an extensively instrumented six-story wood frame building tested in a series of full-scale seismic tests in the final phase of the NEESWood project at the E-Defense facility in Japan. The second case study is a 6-story steel moment resisting frame building located in Burbank, CA, and uses the recorded acceleration data from the 1991 Sierra Madre and 1994 Northridge earthquakes. The third case is a seven-story reinforced concrete structure in Van Nuys, CA, which was severely damaged during the 1994 Northridge earthquake. The results presented in this dissertation constitute the most accurate and the highest resolution seismic response and damage measure estimates obtained for instrumented buildings. The proposed PBM concept will help structural engineers make more informed and swift decisions regarding post-earthquake assessment of critical instrumented building structures, thus improving earthquake resiliency of seismic-prone communities.

<p>This thesis deals with the problem of detecting faults in an environment where the measurements are affected by additive noise. To do this, a residual sensitive to faults is derived and statistical methods are used to distinguish faults from noise. Standard methods for fault detection compare a batch of data with a model of the system using the generalized likelihood ratio. Careful treatment of the initial state of the model is quite important, in particular for short batch sizes. One method to handle this is the parity-space method which solves the problem by removing the influence of the initial state using a projection.</p><p>In this thesis, the case where prior knowledge about the initial state is available is treated. This can be obtained for example from a Kalman filter. Combining the prior estimate with a minimum variance estimate from the data batch results in a smoothed estimate. The influence of the estimated initial state is then removed. It is also shown that removing the influence of the initial state by an estimate from the data batch will result in the parity-space method. To model slowly changing faults, an efficient parameterization using Chebyshev polynomials is given.</p><p>The methods described above have been applied to an Inertial Measurement Unit, IMU. The IMU usually consists of accelerometers and gyroscopes, but has in this work been extended with a magnetometer. Traditionally, the IMU has been used to estimate position and orientation of airplanes, missiles etc. Recently, the size and cost has decreased making it possible to use IMU:s for applications such as augmented reality and body motion analysis. Since a magnetometer is very sensitive to disturbances from metal, such disturbances have to be detected. Detection of the disturbances makes compensation possible. Another topic covered is the fundamental question of observability for fault inputs. Given a fixed or linearly growing fault, conditions for observability are given.</p><p>The measurements from the IMU show that the noise distribution of the sensors can be well approximated with white Gaussian noise. This gives good correspondence between practical and theoretical results when the sensor is kept at rest. The disturbances for the IMU can be approximated using smooth functions with respect to time. Low rank parameterizations can therefore be used to describe the disturbances. The results show that the use of smoothing to obtain the initial state estimate and parameterization of the disturbances improves the detection performance drastically.</p>

<p>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. </p><p>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.</p>

This thesis deals with the problem of detecting faults in an environment where the measurements are affected by additive noise. To do this, a residual sensitive to faults is derived and statistical methods are used to distinguish faults from noise. Standard methods for fault detection compare a batch of data with a model of the system using the generalized likelihood ratio. Careful treatment of the initial state of the model is quite important, in particular for short batch sizes. One method to handle this is the parity-space method which solves the problem by removing the influence of the initial state using a projection. In this thesis, the case where prior knowledge about the initial state is available is treated. This can be obtained for example from a Kalman filter. Combining the prior estimate with a minimum variance estimate from the data batch results in a smoothed estimate. The influence of the estimated initial state is then removed. It is also shown that removing the influence of the initial state by an estimate from the data batch will result in the parity-space method. To model slowly changing faults, an efficient parameterization using Chebyshev polynomials is given. The methods described above have been applied to an Inertial Measurement Unit, IMU. The IMU usually consists of accelerometers and gyroscopes, but has in this work been extended with a magnetometer. Traditionally, the IMU has been used to estimate position and orientation of airplanes, missiles etc. Recently, the size and cost has decreased making it possible to use IMU:s for applications such as augmented reality and body motion analysis. Since a magnetometer is very sensitive to disturbances from metal, such disturbances have to be detected. Detection of the disturbances makes compensation possible. Another topic covered is the fundamental question of observability for fault inputs. Given a fixed or linearly growing fault, conditions for observability are given. The measurements from the IMU show that the noise distribution of the sensors can be well approximated with white Gaussian noise. This gives good correspondence between practical and theoretical results when the sensor is kept at rest. The disturbances for the IMU can be approximated using smooth functions with respect to time. Low rank parameterizations can therefore be used to describe the disturbances. The results show that the use of smoothing to obtain the initial state estimate and parameterization of the disturbances improves the detection performance drastically.

This thesis is concerned with automotive active safety, and a central theme is a new safety function called Emergency Lane Assist (ELA). Automotive safety is often categorised into passive and active safety, where passive safety is concerned with reducing the effects of accidents and active safety aims at avoiding them. ELA detects lane departure manoeuvres that are likely to result in a collision and prevents them by applying a steering wheel torque. The ELA concept is based on traffic accident statistics, i.e., it is designed to give maximum safety based on information about real life traffic accidents. The ELA function puts tough requirements on the accuracy of the information from the sensors, in particular the road shape and the position of surrounding objects, and on robust threat assessment. Several signal processing methods have been developed and evaluated in order to improve the accuracy of the sensor information, and these improvements are also analysed in how they relate to the ELA requirements. Different threat assessment methods are also studied, and a common element in both the signal processing and the threat assessment is that they are based on driver behaviour models, i.e., they utilise the fact that depending on the traffic situation, drivers are more likely to behave in certain ways than others. Most of the methods are general and can be, and hopefully also will be, applied also in other safety systems, in particular when a complete picture of the vehicle surroundings is considered, including information about road and lane shape together with the position of vehicles and infrastructure. All methods in the thesis have been evaluated on authentic sensor data from actual and relevant traffic environments.