This work takes place in the context of distribution grids with high level of distributed generation, for example in microgrids. With high level of distributed generation, it has been shown that selective, fast and sensitive network protection is expected to be more difficult. Furthermore, during system restoration, the accurate fault location could be more challenging to assess, thereby increasing the average outage duration.<p>Thanks to cost reductions and improvement of information and communication technologies, future distribution networks will probably have advanced communication infrastructures and more measurement devices installed in order to manage the increasing complexity of those networks, which is primarily caused by the introduction of distributed generation at the distribution level.<p>Therefore this thesis investigates how the monitoring, protection and fault location functions can be improved by using system-wide measurements, i.e. real-time measurements such as synchronized voltage and current measurements recorded at different network locations. Distributed synchronized measurements bring new perspectives for these three functions: protection and fault location are usually performed with local measurements only and synchronized measurements are not common in monitoring applications. For instance, by measuring distributed generators infeed together with some feeder measurements, the protection is expected to be more sensitive and selective and the fault location to be more accurate.<p>The main contribution of this work is the use of state estimation, which is normally only used for network monitoring, for the protection and the fault location. <p>The distribution system state estimation is first developed using the classical transmission system approach. The impact of the placement of the measurement devices and of a relatively low measurement redundancy on the accuracy, on the bad data detection and on the topology error identification capabilities of the estimator are discussed and illustrated. This results in recommendations on the placement of the meters.<p>Then, a backup protection algorithm using system-wide measurements is presented. The coherence of the measurements and the healthy network model are checked thanks to a linear three-phase state estimation. If the model does not fit to the measurements and if the estimated load is too high or unbalanced, a fault is detected. The advantages of the method are that the voltage measurement redundancy is considered, improving the detection sensitivity, and that load models may be considered in the algorithm, avoiding the need to install measurement devices on every line of the network.<p>Finally, two new impedance-based fault location algorithms using distributed voltage and current recordings are proposed. By defining statistical errors on the measurements and the network parameters, a method to compute a confidence interval of the fault distance estimate is proposed. The fault location accuracy and its sensitivity to the fault conditions (e.g. fault resistance or fault type) and to the different sources of error are assessed on a simulated distribution system. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

State estimation is an important tool for power system monitoring and the present study involves integrating phasor measurement units in the state estimation process. Based on measurements taken throughout the network, the role of a state estimator is to estimate the state variables of the power system while checking that these estimates are consistent with the measurement set. In the case of power system state estimation, the state variables are the voltage phasors at each network bus.\\<p><p>The classical state estimator currently used is based on SCADA (Supervisory Control and Data Acquisition) measurements. Weaknesses of the SCADA measurement system are the asynchronicity of the measurements, which introduce errors in the state estimation results during dynamic events on the electrical network.\\<p><p>Wide-area monitoring systems, consisting of a network of Phasor Measurement Units (PMU) provide synchronized phasor measurements, which give an accurate snapshot of the monitored part of the network at a given time. The objective of this thesis is to integrate PMU measurements in the state estimator. The proposed state estimators use PMU measurements exclusively, or both classical and PMU measurements.\\ <p><p>State estimation is particularly useful to filter out measurement noise, detect and eliminate bad data. A sensitivity analysis to measurement errors is carried out for a state estimator using only PMU measurements and a classical state estimator. Measurement errors considered are Gaussian noise, systematic errors and asynchronicity errors. Constraints such as zero injection buses are also integrated in the state estimator. Bad data detection and elimination can be done before the state estimation, as in pre-estimation methods, or after, as in post-estimation methods. For pre-estimation methods, consistency tests are used. Another proposed method is validation of classical measurements by PMU measurements. Post-estimation is applied to a measurement set which has asynchronicity errors. Detection of a systematic error on one measurement in the presence of Gaussian noise is also analysed. \\<p><p>The state estimation problem can only be solved if the measurements are well distributed over the network and make the network observable. Observability is crucial when trying to solve the state estimation problem. A PMU placement method based on metaheuristics is proposed and compared to an integer programming method. The PMU placement depends on the chosen objective. A given PMU placement can provide full observability or redundancy. The PMU configuration can also take into account the zero injection nodes which further reduce the number of PMUs needed to observe the network. Finally, a method is proposed to determine the order of the PMU placement to gradually extend the observable island. \\<p><p>State estimation errors can be caused by erroneous line parameter or bad calibration of the measurement transformers. The problem in both cases is to filter out the measurement noise when estimating the line parameters or calibration coefficients and state variables. The proposed method uses many measurement samples which are all integrated in an augmented state estimator which estimates the voltage phasors and the additional parameters or calibration coefficients. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

Orientador: Madson Cortes de Almeida / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-27T18:54:32Z (GMT). No. of bitstreams: 1 YucraCcahuana_MiguelAngel_M.pdf: 1685093 bytes, checksum: d470831fabdff66aa7a9d998da9391db (MD5) Previous issue date: 2015 / Resumo: Nas últimas décadas, a utilização de Unidades de Medições Fasoriais (Phasor Measurements Units - PMU) em sistemas de transmissão de energia elétrica vem sendo amplamente discutida. De acordo com a literatura, diversas aplicações podem ser beneficiadas pelo uso destas medidas. Em comparação com as medidas convencionais disponibilizadas pelos Sistema de Controle e Aquisição de Dados (SCADA), as medições fasoriais apresentam maior precisão, maior taxa de amostragem e são grandezas fasoriais sincronizadas, enquanto que as medições do sistema SCADA são medidas de módulo, não sincronizadas e de menor precisão. Para que as aplicações em regime permanente possam se beneficiar das medidas providas pelas PMUs, essas medidas devem ser filtradas por um processo de estimação de estado. Apesar da precisão e da confiabilidade, as medidas provenientes de PMUs ainda podem conter erros causados no processo de medição, os quais precisam ser eliminados antes que as informações providas pelas PMUs sejam usadas nas tarefas de controle e operação da rede. Apesar de todos os investimentos e desenvolvimentos relacionados às PMUs, atualmente, devido ao grande custo associado à instalação dessas unidades, não se considera viável o desenvolvimento de sistemas de medição que permitam que redes sejam totalmente observáveis apenas com o uso de medições fasoriais. A literatura apresenta algumas técnicas capazes de considerar simultaneamente medidas fasoriais e medidas convencionais no processo de estimação de estado. Neste trabalho, são apresentados e avaliados os principais estimadores de estado de uma e duas fases disponíveis na literatura. Além disso, o estimador tradicional, sem a presença de medidas fasoriais, foi usado como base de comparação. Na primeira etapa do processo de avaliação é analisada a qualidade dos estimadores de estado na presença de medidas perfeitas e na presença de medidas contendo erros gaussianos aleatórios. Erros gaussianos aleatórios são os erros típicos dos processos de medição e, portanto, são considerados aceitáveis. Na segunda etapa do processo de avaliação, os estimadores são avaliados na presença de medidas contendo erros gaussianos aleatórios e erros grosseiros. Os erros grosseiros, ao contrário dos erros gaussianos, não são considerados aceitáveis e devem ser removidos durante o processo de estimação de estado. A fim de tornar a análise mais ampla e realista, os três principais mecanismos de cálculo de desvios padrão propostos na literatura são considerados na avaliação do estimadores. Assim, são considerados cenários com desvios padrão fixos, desvios padrão calculados em função dos valores das medidas e desvios padrão calculados em função dos valores medidos e dos fundos de escala dos medidores. Para a detecção e identificação de erros grosseiros é adotado o método do Maior Resíduo Normalizado, já que este é o mecanismo mais confiável, robusto e aceito para tal finalidade. São apresentados testes realizados na rede de 14 barras do IEEE. Esta rede é amplamente adotada em teses e artigos especializados da área, o que facilita a comparações de resultados / Abstract: In last decades, the use of Phasor Measurement Units (PMU) in electrical power transmission systems has been widely discussed. According to the literature, several applications may be benefited with the use of these measurements. When they are compared with conventional measurements provided by Supervisory control and data adquisition (SCADA), phasor measurements are more accurate, have a high sampling rate and are synchronized, while the SCADA measurements only provide magnitude measurements, are not synchronized and are less accurate. For applications in steady operation can be benefited from the measurements provided by PMUs, these measurements must be filtered by a state estimation process. Despite the accuracy and reliability, measurements from PMUs may contain errors caused by the measurement process, they must be eliminated before the informations provided by PMUs are used in control tasks and network operation. Moreover, despite all the investments and developments related to PMUs, currently, because of the cost associated to the installation of these units; it is not considered feasible the development of measurement systems that allow electric networks to be fully observable only with the use of phasor measurements. The literature presents some techniques that are able to consider simultaneously phasor measurements and conventional measurements in the state estimation process. In this work, the main state estimators of one and two phases available in the literature are presented and evaluated. Moreover, the traditional state estimator was used to compare results. In the first part of evaluation process, the quality of state estimators in the presence of perfect measurements and measurements containing random gaussian errors is analyzed. Gaussian random errors are typical of measurement processes and therefore, they are considered acceptable. In the second part, the state estimators are evaluated in the presence of measurements containing random gaussian errors and gross errors. Gross errors, unlike the gaussian errors, are not acceptable and must be removed during the state estimation process. In order to make the analysis more comprehensive and realistic, the three main mechanisms of standard deviation calculation that are proposed in the literature are considered in state estimators evaluation. So, scenarios with fixed standard deviations, standard deviations calculated according to measurement values and standard deviations calculated according to measurements values and the full scale of meters are considered. For the detection and identification of gross errors, the large normalized residual test (LNR) is adopted, since this test is more reliable, robust and acceptable for this purpose. Tests in IEEE14 network are carried out. This network is widely adopted in theses and articles specialized in the field which facilitates the comparison of results / Mestrado / Energia Eletrica / Mestre em Engenharia Elétrica

Dans le contexte des systèmes dynamiques, cette thèse développe des techniques d'estimation d'état ensemblistes pour différentes classes de systèmes. On considère pour cela le cas d'un système standard linéaire invariant dans le temps soumis à des perturbations, des bruits de mesure et des incertitudes inconnus, mais bornés. Dans une première étape, une technique d'estimation d'état ellipsoïdale est étendue, puis appliquée sur un modèle d'octorotor utilisé dans un contexte radar. Une extension de cette approche ellipsoïdale d'estimation d'état est proposée pour des systèmes descripteurs. Dans la deuxième partie, nous proposons une méthode fondée sur la minimisation du P-rayon d'un zonotope, appliquée à un modèle d'octorotor. Cette méthode est ensuite étendue pour traiter les systèmes affines par morceaux. Dans la continuité des approches précédentes, un nouveau filtre de Kalman sous contraintes zonotopiques est proposé dans la dernière partie de cette thèse. En utilisant la forme duale d'un problème d'optimisation, l'algorithme projette l'état sur un zonotope qui forme l'enveloppe de l'ensemble des contraintes auxquelles l'état est soumis. La complexité de l'algorithme est ensuite améliorée en remplaçant le zonotope initial par une forme réduite en limitant son nombre de générateurs. / In the context of dynamical systems, this thesis focuses on the development of robust set-membership state estimation procedures for different classes of systems. We consider the case of standard linear time-invariant systems, subject to unknown but bounded perturbations and measurement noises. The first part of this thesis builds upon previous results on ellipsoidal set-membership approaches. An extended ellipsoidal set-membership state estimation technique is applied to a model of an octorotor used for radar applications. Then, an extension of this ellipsoidal state estimation approach is proposed for descriptor systems. In the second part, we propose a state estimation technique based on the minimization of the P-radius of a zonotope, applied to the same model of the octorotor. This approach is further extended to deal with piecewise affine systems. In the continuity of the previous approaches, a new zonotopic constrained Kalman filter is proposed in the last part of this thesis. By solving a dual form of an optimization problem, the algorithm projects the state on a zonotope forming the envelope of the set of constraints that the state is subject to. Then, the computational complexity of the algorithm is improved by replacing the original possibly large-scale zonotope with a reduced form, by limiting its number of generators.

Dans cette thèse, nous aborderons deux aspects fondamentaux qui se posent dans les systèmes de commande modernes du fait de l'interaction entre des processus en temps continu et des dispositifs numériques: la synthèse de lois de commande en présence de quantificateurs et l'estimation d'état en présence de mesures sporadiques. Une des caractéristiques principales de cette thèse consiste également à proposer des méthodes constructives pour résoudre les problèmes envisagés. Plus précisément, pour répondre à cette exigence, nous allons nous tourner vers une approche basée sur les inégalités matricielles linéaires (LMI). Dans la première partie de la thèse, nous proposons un ensemble d'outils constructifs basés sur une approche LMI, pour l'analyse et la conception de systèmes de commande quantifiés impliquant des modèles et des correcteurs linéaires. L'approche est basée sur l'utilisation des inclusions différentielles qui permet de modéliser finement le comportement de la boucle fermée et ainsi d'obtenir des résultats intéressants. Dans la seconde partie de la thèse, inspirés par certains schémas d'observation classiques présentés dans la littérature, nous proposons deux observateurs pour l'estimation de l'état d'un système linéaire en présence de mesures sporadiques, c'est-à-dire prenant en compte la nature discrète des mesures disponibles. De plus, en se basant sur une des deux solutions présentées, une architecture de commande basée observateur est proposée afin de stabiliser asymptotiquement un système linéaire en présence à la fois de mesures sporadiques et d'un accès intermittent à l'entrée de commande du système. / In this dissertation, two fundamental aspects arising in modern engineered control systems will be addressed:On the one hand, the presence of quantization in standard control loops. On the other hand, the state estimation in the presence of sporadic available measurements. These two aspects are addressed in two different parts. One of the main feature of this thesis consists of striving to derive computer-aided tools for the solution to the considered problems. Specifically, to meet this requirement, we revolve on a linear matrix inequalities (LMIs) approach. In the first part, we propose a set of LMI-based constructive Lyapunov-based tools for the analysis and the design of quantized control systems involving linear plants and linear controllers. The entire treatment revolves on the use of differential inclusions as modeling tools, and on stabilization of compact sets as a stability notion. In the second part of the thesis, inspired by some of the classical observation schemes presented in the literature of sampled-data observers, we propose two observers to exponentially estimate the state of a linear system in the presence of sporadic measurements. In addition, building upon one of the two observers, an observer-based controller architecture is proposed to asymptotically stabilize a linear plant in the presence of sporadic measurements and intermittent input access.

<p dir="ltr">Resilient engine operation hugely depends on proper functioning of the engine’s sensors, enabling efficient feedback control of the engine systems operation. When the sensors on the engine measure a physical quantity incorrectly, it leads the engine control system to determine that the sensor measuring the physical quantity has failed. This failure may be attributed to a sensor stick failure, bias failure, drift failure, or failure occurring due to physical wear and tear of the sensor. Failure of crucial engine sensors may have adverse effects on engine operation, and in most cases leading into a limp home mode or a torque limitation mode. This affects the engine performance and efficiency. The engine under study in this work is a medium duty marine engine with diesel fuel. Sensor failures in the middle of a marine operation can hugely impact its mission. Therefore, fault tolerant control systems are essential to counter these challenges occurring due to sensor failures. In this thesis, an advanced nonlinear fault detection and state estimation algorithm is developed and implemented on a GT-Power engine model, employing a sophisticated co-simulation approach. The focus is on a 6.7L Cummins diesel engine, for which a detailed nonlinear state space model is constructed. This model accurately replicates critical engine parameters, such as pressures, temperatures, and engine speed, by integrating various submodels. These sub-models estimate key parameters like cylinder inlet charge flow, valve flow, cylinder outlet temperature, turbocharger turbine flow, and charge air cooler flow. To assess the model’s accuracy and reliability, it is rigorously validated against a truth reference GT-Power engine model. The results demonstrate exceptional performance, with the nonlinear model exhibiting a minimal percentage performance error of less than 5% under steady-state conditions and less than 15% during transient conditions. The core of the Fault Detection and State Estimation (FDSE) modules consists of a bank of Extended Kalman Filters (EKF). These filters are meticulously designed to estimate vital engine states, generate residuals, and assess these residuals even in the presence of process and measurement noise. This approach enables the detection of sensor faults and facilitates controller reconfiguration, ensuring the engine’s robustness in the face of unexpected sensor failures. Crucially, the nonlinear physics-based model serves as the foundation for the state transition functions utilized in the design of the observer bank. Residuals generated by the EKFs are evaluated using both fixed and adaptive thresholding techniques masking the sensor faults at the time step at which it is detected, ensuring robust performance not only in steady-state conditions but also during varying transient load conditions. To comprehensively evaluate the system’s resilience in practical scenarios, multiple sensor stuck failures are introduced into the GT-Power model. A software-in-the-loop co-simulation strategy is meticulously established, employing both the GT-Power truth reference engine model and the nonlinear Fault Detection and State Estimation (FDSE) model within the Simulink environment. This unique co-simulation approach provides a platform to assess the FDSE performance and its effect on engine performance in simulated sensor fault scenarios. The FDSE module is able to detect sensor failures which deviate at least 5% from their actual values. The percentage estimation error is less than 10% under steady state conditions and less than 20% under transient load conditions. Ultimately, this process creates analytical redundancy, not only forming the basis of state estimation but also empowering the engine to maintain its performance in the presence of sensor faults.</p>

<p dir="ltr">State estimation is a type of an <i>inverse problem</i> in which some amount of observed data needs to be processed using computer algorithms (which are designed using analytical techniques) to infer or reconstruct the underlying model that produced the data. Due to the ubiquity of data and interconnected control systems in the present day, many engineering domains have become replete with inverse problems that can be formulated as state estimation problems. The interconnectedness of these control systems imparts the associated state estimation problems with distinctive structural properties that must be taken into consideration. For instance, the observed data could be high-dimensional and have a dependency structure that is best described by a graph. Furthermore, the control systems of today interface with each other and with the internet, bringing in new possibilities for large-scale collaborative sensor fusion, while also (potentially) introducing new sources of disturbances, faults, and cyberattacks. </p><p dir="ltr">The main thesis of this document is to investigate the unique challenges related to the issues of robustness, resilience (to faults and cyberattacks), and scalability of state estimation algorithms. These correspond to research questions such as, <i>"Does the state estimation algorithm retain its performance when the measurements are perturbed by unknown disturbances or adversarial inputs?"</i> and <i>"Does the algorithm have any bottlenecks that restrict the size/dimension of the problems that it could be applied to?".</i> Most of these research questions are motivated by a singular domain of application: autonomous navigation of unmanned aerial vehicles (UAVs). Nevertheless, the mathematical methods and research philosophy employed herein are quite general, making the results of this document applicable to a variety of engineering tasks, including anomaly detection in time-series data, autonomous remote sensing, traffic monitoring, coordinated motion of dynamical systems, and fault-diagnosis of wireless sensor networks (WSNs), among others.</p>

As the capabilities of autonomous systems have increased so has interest in utilizing teams of autonomous systems to accomplish tasks more efficiently. This dissertation takes steps toward enabling the cooperation of unmanned systems in scenarios that are challenging, such as GPS-denied or perceptually aliased environments. This work begins by developing a cooperative navigation framework that is scalable in the number of agents, robust against communication latency or dropout, and requires little a priori information. Additionally, this framework is designed to be easily adopted by existing single-agent systems with minimal changes to existing software and software architectures. All systems in the framework are validated through Monte Carlo simulations. The second part of this dissertation focuses on making cooperative navigation robust in challenging environments. This work first focuses on enabling a more robust version of pose graph SLAM, called cycle-based pose graph optimization, to be run in real-time by implementing and validating an algorithm to incrementally approximate a minimum cycle basis. A new algorithm is proposed that is tailored to multi-agent systems by approximating the cycle basis of two graphs that have been joined. These algorithms are validated through extensive simulation and hardware experiments. The last part of this dissertation focuses on scenarios where perceptual aliasing and incorrect or unknown data association are present. This work presents a unification of the framework of consistency maximization, and extends the concept of pairwise consistency to group consistency. This work shows that by using group consistency, low-degree-of-freedom measurements can be rejected in high-outlier regimes if the measurements do not fit the distribution of other measurements. The efficacy of this method is verified extensively using both simulation and hardware experiments.

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Traffic State Estimation (TSE) is a vital component in traffic control which requires an accurate viewof the current traffic situation. Since there is no full sensor coverage and the collected measurementsare inflicted with random noise, statistical estimation techniques are necessary to accomplish this.Common methods, which have been used in highway applications for several decades, are state-spacemodels in the form of Kalman Filters and Particle Filters. These methods are forms of BayesianFilters, and rely on transition models to describe the system dynamics, and observation models torelate collected measurements to the current state. Reliable estimation of traffic in urban environmentshas been considered more difficult than in highways owing to the increased complexity.This MsC thesis build upon previous research studying the use of non-parametric Gaussian Processtransition and measurement models in an extended Kalman Filter to achieve short-term TSE. To dothis, models requiring different feature sets are developed and analysed, as well as a hybrid approchcombining non-parametric and parametric models through an analytical mean function based on vehicleconservation law. The data used to train and test the models was collected in a simulated signalizedintersection constructed in SUMO.The presented results show that the proposed method has potential to performing short-term TSE inthis context. A strength in the proposed framework comes from the probabilistic nature of the GaussianProcesses, as it removes the need to manually calibrate the filter parameters of the Kalman Filter. Themean absolute error (MAE) lies between one and five vehicles for estimation of a one hour long dataseries with varying traffic demand. More importantly, the method has desirable characteristics andcaptures short-term fluctuations as well as larger scale demand changes better than a previously proposedmodel using the same underlying framework. In the cases with poorer performance, the methodprovided estimates unrelated to the system dynamics as well as large error bounds. While the causefor this was not determined, several hypotheses are presented and analysed. These results are takento imply that the combination of BF and GP models has potential for short-term TSE in a signalizedintersection, but that more work is necessary to provide reliable algorithms with known bounds. In particular,the relative ease of augmenting an available analytical model, built on conventional knowledgein traffic modelling, with a non-parametric GP is highlighted.