Runtime dynamic reconfiguration of field-programmable gate arrays (FPGAs) and devices incorporating microprocessors and FPGA has been successfully utilized to increase performance and reduce power consumption. While previous methods have been successful, they typically do not consider the runtime behavior of the application that can be significantly affected by variations in data inputs, user interactions, and environmental conditions. In this dissertation, we present a dynamically reconfigurable system and design methodology that optimizes performance and power consumption by determining which coprocessors to implement with an FPGA based upon the current application behavior.For dynamically reconfigurable systems, in which the selection of hardware coprocessors to implement within the FPGA is determined at runtime, online estimation methods are essential to evaluate the performance and power consumption impact of the hardware coprocessor selection. We present a base profile assisted online system-level performance and power estimation framework for estimating the speedup and power consumption of dynamically reconfigurable embedded systems.Importantly though, complex interactions between multiple application tasks, non-deterministic execution behavior, and effects of operating system scheduling introduce significant challenges. To address these, we further present an adaptive online performance and power estimation framework suing kernel speedup coefficient adaptation that monitors and adapts the changing application and system behavior for multitasked applications. By exhaustively examining predefined voltage and frequency settings for the microprocessor and hardware kernels, the potential speedup and power reduction can be effectively estimated for each configuration and voltage/frequency settings. These estimates can be utilized to determine the optimal system configuration. At the same time, the kernel speedup coefficients for each kernel can be dynamically updated to account for the difference between the estimated and actual performance measured at runtime.Finally, in order to quickly determine kernel selection and voltage and frequency settlings, we present an efficient, online heuristic performance and power estimation framework that significantly decreases execution time at the cost of a small increase in power consumption. This online heuristic estimation framework achieves significant power reduction compared to software only implementation without performance degradation.

No / Model based control of automotive engines for fuel economy and pollution minimization depends on accuracy of models used. A number of mathematical models of automotive engine processes are available for this purpose but critical model parameters are difficult to obtain and generalize. This paper presents a novel method of online estimation of discharge coefficient of throttle body at the intake manifold of gasoline engines. The discharge coefficient is taken to be a varying parameter. Air mass flow across the throttle body is a critical variable in maintaining a closer to stoichiometric air fuel ratio; which is necessary to minimize the pollution contents in exhaust gases. The estimation method is based on sliding mode technique. A classical first Sliding mode observer is designed to estimate intake manifold pressure and the model uncertainty arising from the uncertain and time varying discharge coefficient is compensated by the discontinuity/switching signal of sliding mode observer. This discontinuity is used to compute coefficient of discharge as a time varying signal. The discharge coefficient is used to tune/correct the intake manifold model to engine measurements. The resulting model shows a very good agreement with engine measurements in steady as wells transient state. The stability of the observer is shown by Lyapunov direct method and the validity of the online estimation is successfully demonstrated by experimental results. OBD-II (On Board Diagnostic revision II) based sensor data acquisition from the ECU (Electronic Control Unit) of a production model vehicle is used. The devised algorithm is simple enough to be designed and implemented in a production environment. The online estimation of parameter can also be used for engine fault diagnosis work. (c) 2012 Elsevier B.V. All rights reserved.

Préfigurant la prochaine grande étape dans le domaine de la navigation, la géolocalisation intra-muros est un domaine de recherche très actif depuis quelques années. Alors que la géolocalisation est entrée dans le quotidien de nombreux professionnels et particuliers avec, notamment, le guidage routier assisté, les besoins d'étendre les applications à l'intérieur se font de plus en plus pressants. Cependant, les systèmes existants se heurtent à des contraintes techniques bien supérieures à celles rencontrées à l'extérieur, la faute, notamment, à la propagation chaotique des ondes électromagnétiques dans les environnements confinés et inhomogènes. Nous proposons dans ce manuscrit une approche statistique du problème de géolocalisation d'un mobile à l'intérieur d'un bâtiment utilisant les ondes WiFi environnantes. Ce manuscrit s'articule autour de deux questions centrales : celle de la détermination des cartes de propagation des ondes WiFi dans un bâtiment donné et celle de la construction d'estimateurs des positions du mobile à l'aide de ces cartes de propagation. Le cadre statistique utilisé dans cette thèse afin de répondre à ces questions est celui des modèles de Markov cachés. Nous proposons notamment, dans un cadre paramétrique, une méthode d'inférence permettant l'estimation en ligne des cartes de propagation, sur la base des informations relevées par le mobile. Dans un cadre non-paramétrique, nous avons étudié la possibilité d'estimer les cartes de propagation considérées comme simple fonction régulière sur l'environnement à géolocaliser. Nos résultats sur l'estimation non paramétrique dans les modèles de Markov cachés permettent d'exhiber un estimateur des fonctions de propagation dont la consistance est établie dans un cadre général. La dernière partie du manuscrit porte sur l'estimation de l'arbre de contextes dans les modèles de Markov cachés à longueur variable. / Foreshadowing the next big step in the field of navigation, indoor geolocation has been a very active field of research in the last few years. While geolocation entered the life of many individuals and professionals, particularly through assisted navigation systems on roads, needs to extend the applications inside the buildings are more and more present. However, existing systems face many more technical constraints than those encountered outside, including the chaotic propagation of electromagnetic waves in confined and inhomogeneous environments. In this manuscript, we propose a statistical approach to the problem of geolocation of a mobile device inside a building, using the WiFi surrounding waves. This manuscript focuses on two central issues: the determination of WiFi wave propagation maps inside a building and the construction of estimators of the mobile's positions using these propagation maps. The statistical framework used in this thesis to answer these questions is that of hidden Markov models. We propose, in a parametric framework, an inference method for the online estimation of the propagation maps, on the basis of the informations reported by the mobile. In a nonparametric framework, we investigated the possibility of estimating the propagation maps considered as a single regular function on the environment that we wish to geolocate. Our results on the nonparametric estimation in hidden Markov models make it possible to produce estimators of the propagation functions whose consistency is established in a general framework. The last part of the manuscript deals with the estimation of the context tree in variable length hidden Markov models.

In standard Real-time Optimization (RTO) implementations, the plant needs to be suciently steady in order to update the RTO model parameters reliably. However, this condition is seldom found in practice. Moreover, because the RTO model is only updated when the plant enters a stationary condition, the optimizer is likely to be out of phase with highly perturbed plants. The main contribution of the thesis is the proposal of an alternative RTO approach, called Real-time Optimization with Persistent Adaptation (ROPA), which integrates on-line parameter estimation in the optimization cycle, avoiding the steady-state (SS) detection step. Instead of predicting the SS, the online estimator keeps the model up-to-date with the plant and allows running the economic optimization at any time, even instants after implementing the current RTO decisions. ROPA provides an intermediary solution between static and dynamic optimization schemes. While it approximates the optimal trajectory, ROPA enables the use of well-established static RTO commercial solutions. Furthermore, the new approach is the key for decoupling the model estimation problem in order to achieve plant-wide optimization. Another contribution of the thesis is to provide several case studies in which ROPA is tested and compared with the standard RTO implementation: a Williams-Otto reactor, a Fluid Catalyst Cracking unit and a separation-reaction system. The idea is to illustrate ROPA convergence properties and how the plant-wide optimum is achieved by asynchronously updating the global plant model. The results show that ROPA is able to track the stationary (plant-wide) optimum. In addition, they conrm that the renement of the prediction capacity, by decreasing the time between two sequential optimization, enhances the disturbance detection of the optimization cycle and leads to a better economic performance. / Na implementação padrão de otimização em tempo real (RTO, do inglês real-time optimization), a planta deve estar suficientemente estável para que os parâmetros do modelo usado no RTO sejam estimados com precisão. Contudo, esta condição é raramente encontrada na prática. Alám disso, devido ao fato de o modelo usado no RTO ser atualizado somente quando a planta entra em estado estacionário, é provável que o otimizador esteja fora de fase quando implementado em plantas com alta frequência de distúrbios. A principal contribuição desta tese e o desenvolvimento de uma metodologia alternativa de RTO chamada otimização em tempo real com atualização persistente de parâmetros (ROPA, do inglês real-time optimization with persistent adaptation). A nova metodologia integra estimadores em tempo real ao ciclo de otimização, evitando assim a necessidade da etapa de detecção de estado estacionário. Ao invés de identificá-lo, o estimador em tempo real mantém o modelo atualizado com a planta e permite que se execute a otimização econômica em tempos arbitrários, mesmo instantes depois da implementação da decisão ótima calculada anteriormente pelo RTO. ROPA provê uma solução intermediária entre a otimização estática e dinâmica. Ao mesmo tempo que aproxima a trajetória ótima, ela permite o uso de soluções comerciais já estabelecidas de RTO estacionário. Também, a nova metodologia é a chave para o desacoplamento do problema de estimação a fim de se atingir o ótimo global da planta. Uma contribuição adicional da tese é a apresentação de três casos de estudo que testam a ROPA e comparam sua performance à implementação padrão de RTO: um reator Williams-Otto, uma unidade de craqueamento catalítico e um sistema de separação-reação. A ideia principal e ilustrar as propriedades de convergência da nova metodologia e como a atualização assíncrona do modelo global da planta pode ser usada para atingir o ótimo da planta como um todo. Os resultados mostram que a ROPA é capaz de alcançar o ótimo estacionário da planta. Adicionalmente, o refinamento da capacidade de predição através da diminuição do tempo entre duas execuções sequenciais do otimizador melhora a capacidade de detecção de distúrbios do ciclo de otimização assim como a performance econômica.

Cette thèse s'intéresse à l'adaptation des lois de pilotage d'un avion de transport civil aux différentes incertitudes qui peuvent affecter sa dynamique. Le procédé de pilotage adaptatif est censé fonctionner en temps réel à bord de l'avion afin d'optimiser la performance boucle fermée en fonction des conditions dans lesquelles il évolue. Les incertitudes peuvent être liées à la méconnaissance des conditions de vol (par exemple la vitesse et l'altitude), à des non-linéarités aérodynamiques inconnues ou encore à la méconnaissance du pilote aux commandes. Les procédés adaptatifs qui répondent à ces problèmes se doivent d'être performants sur l'ensemble du domaine opérationnel de l'avion en présence de perturbations réalistes. D'autres contraintes spécifiques peuvent être ajoutées en fonction du contexte (par exemple des charges limites, la stabilité aéroélastique, etc.). Plusieurs méthodes adaptatives sont testées afin d'adapter le système aux larges incertitudes qui le composent. Elles associent en général un estimateur en ligne (aussi appelé loi de mise-à-jour) à une loi de commande structurée. La synthèse de ces deux éléments peut être réalisée simultanément pour les méthodes adaptatives dites " directes ", comme par exemple le Model Reference Adaptative Control qui utilise la stabilité au sens de Lyapounov. Mais cette synthèse peut aussi être découplée pour les méthodes adaptatives dites "indirectes", ce qui offre un large choix de techniques pour chaque élément (comme les Moindres Carrés pour l'estimation de paramètres physiques incertains et la synthèse sous forme LFR pour le correcteur). Le choix de la méthode dépend fortement du contexte applicatif et des nombreuses contraintes associées. Trois applications sont au cœur de ce mémoire. Elles traitent de l'ajustement de lois de guidage à un modèle pilote inconnu, du contrôle longitudinal de non-linéarités de l'avion, et de la mise au point de lois longitudinale et latérale de pilotage manuel qui s'adaptent à des conditions de vol inconnues. Des méthodes avancées d'analyse linéaire et non-linéaire (dérivées de la µ-analyse et d'algorithmes d'optimisation) sont aussi mises en place pour valider ces systèmes sophistiqués adaptatifs en temps réel. D'une façon générale, les méthodes adaptatives indirectes ont donné le plus de satisfaction. Leur performance est aussi bonne que celle des méthodes directes, mais le fait qu'elles estiment en ligne des paramètres physiques facilite la surveillance temps réel du procédé adaptatif et sa validation. / This thesis deals with adapting flight control laws of a civil transport aircraft to various incertainties which can affect its behaviour. The adaptive flight control system is supposed to run in real time onboard the airplane so that its closed-loop performance is optimized with respect to the current conditions. These incertainties may be linked to unknown flight conditions (e.g. unknown airspeed and altitude), or unknown aerodynamics non-linearities or even unknown behaviour of the pilot in command. The adaptive schemes that are derived to answer these problems must be valid on the whole flight envelope with realistic disturbances but other additional contraints may exist depending on the context (e.g. loads limits, aeroelastic stability, etc.). To accommodate for large uncertainties on the system, adaptive methods are investigated. They usually combine an online estimator (also called an update law) with a structured flight control law. The synthesis of both elements may be simultaneous on 'direct' adaptive methods, e.g. on Model Reference Adaptive Control, using Lyapunov's stability theory. But it can also be decoupled on 'indirect' adaptive methods, giving a full spectrum of techniques for both elements (such as Least-Squares for estimating unknown physical parameters and the LFR framework for designing controllers). The choice of a specific method really depends on the application context and the related constraints.Three applications are the core of this report. They deal with adjusting guidance law to the pilot's unknown behaviour, controlling a longitudinal non-linearity, and providing manual longitudinal and lateral flight control laws which adapt to unknown flight conditions. Advanced linear and non-linear analysis techniques (based on µ-analysis or on optimization algorithms) are also applied to validated these sophisticated real-time adaptive systems. Results showed that indirect adaptive schemes were generally the most satisfactory. Their performance is similar to the one of direct schemes but as indirect methods provide physical parameter estimates, real-time monitoring and offline validation seem quite easier.

Driving with tyres not appropriate for the actual conditions can not only lead to accidents related to the tyres, but also cause detrimental effects on the environment via emission of rubber particles if the driving conditions are causing an unexpectedly high amount of tread wear. Estimating tyre performance in an online setting is therefore of interest, and the feasibility to estimate friction performance, velocity performance, and tread wear utilizing available information from the automotive grade sensors is investigated in this thesis. For the friction performance, a trend analysis is performed to investigate the correlation between tyre stiffness and friction potential. Given that there is a correlation, a model is derived based on the trend having a stiffness parameter as an input in order to predict the friction performance. Tendencies for a linear trend is shown, and a linear regression model is fitted to data and is evaluated by calculating a model fit and studying the residuals. Having a model fit of $80\%$, the precision of the expected values stemming from the proposed model is concluded to be fairly low, but still enough to roughly indicate the friction performance in winter conditions. A tread wear model that can estimate the amount of abrasive wear is also derived, and the proposed model only utilizes available information from the automotive grade sensors. Due to the model having a parameter that is assumed to be highly tyre specific, only a relative wear difference can be calculated. The model is evaluated in a simulation environment by its ability to indicate if a tyre is under the influence of a higher wear caused by a higher ambient temperature. The results indicates that the model is insufficient in an online setting and cannot accurately describe the phenomena of softer tyres having a larger amount of wear caused by a high ambient temperature compared to stiffer tyres. Lastly, a double lane change test (ISO 3888-2) is conducted to determine the critical velocity for cornering manoeuvres, which defines the velocity performance. The test was executed for six different sets of tyres, two of each type (winter, all-season, and summer). The approach to estimate the velocity performance in an online setting is analogue to that of the friction performance, and a trend analysis is performed to investigate the correlation between longitudinal tyre stiffness and the critical velocity. The results are rather unexpected and shows no substantial differences in velocity performance, even though the tyre-road grip felt distinctively worse for the softer tyres according to the driver. It is concluded that the bias stemming from the professional driver's skills might have distorted the results, and that another approach might need to be considered in order to estimate this performance.

Les travaux présentés dans cette thèse concernent le problème d'identification des systèmes à retards et d'une certaine classe de systèmes hybrides appelés systèmes "impulsifs".Dans la première partie, un algorithme d'identification rapide a été proposé pour les systèmes à entrée retardée. Il est basé sur une méthode d'estimation distributionnelle non asymptotique initiée pour les systèmes sans retard. Une telle technique mène à des schémas de réalisation simples, impliquant des intégrateurs, des multiplicateurs et des fonctions continues par morceaux polynomiales ou exponentielles. Dans le but de généraliser cette approche pour les systèmes à retard, trois exemples d'applications ont été étudiées. La deuxième partie a été consacrée à l'identification des systèmes impulsifs. En se basant sur le formalisme des distributions, une procédure d'identification a été élaborée afin d'annihiler les termes singuliers des équations différentielles représentant ces systèmes. Par conséquent, une estimation en ligne des instants de commutations et des paramètres inconnus est prévue indépendamment des lois de commutations. Des simulations numériques d'un pendule simple soumis à des frottements secs illustrent notre méthodologie / This PhD thesis concerns the problem of identification of the delays systems and the continuous-time systems subject to impulsive terms.Firstly, a fast identification algorithm is proposed for systems with delayed inputs. It is based on a non-asymptotic distributional estimation technique initiated in the framework of systems without delay. Such technique leads to simple realization schemes, involving integrators, multipliers andContribution to the identification of time delays systems and a class of hybrid systems piecewise polynomial or exponential time functions. Thus, it allows for a real time implementation. In order to introduce a generalization to systems with input delay, three simple examples are presented.The second part deals with on-line identification of continuous-time systems subject to impulsive terms. Using a distribution framework, a scheme is proposed in order to annihilate singular terms in differential equations representing a class of impulsive systems. As a result, an online estimation of unknown parameters is provided, regardless of the switching times or the impulse rules. Numerical simulations of simple pendulum subjected to dry friction are illustrating our methodology

The idea behind this report is based on the fact that it is not only the number of users in the traffic network that is increasing, the number of connected devices such as probe vehicles and mobile sources has increased dramatically in the last decade. These connected devices provide large-scale mobility data and new opportunities to analyze the current traffic situation as they traverse through the network and continuously send out different types of information like Global Positioning System (GPS) data and Mobile Network Data (MND). Travel demand is often described in terms of an Origin Destination (OD) matrix which represents the number of trips from an origin zone to a destination zone in a geographic area. The aim of this master thesis is to develop and evaluate a data-driven method for estimation of dynamic OD matrices using unsupervised learning, sensor fusion and large-scale mobility data. Traditionally, OD matrices are estimated based on travel surveys and link counts. The problem is that these sources of information do not provide the quality required for online control of the traffic network. A method consisting of an offline process and an online process has therefore been developed. The offline process utilizes historical large-scale mobility data to improve an inaccurate prior OD matrix. The online process utilizes the results and tuning parameters from the offline estimation in combination with real-time observations to describe the current traffic situation. A simulation study on a toy network with synthetic data was used to evaluate the data-driven estimation method. Observations based on GPS data, MND and link counts were simulated via a traffic simulation tool. The results showed that the sensor fusion algorithms Kalman filter and Kalman filter smoothing can be used when estimating dynamic OD matrices. The results also showed that the quality of the data sources used for the estimation is of high importance. Aggregating large-scale mobility data as GPS data and MND by using the unsupervised learning method Principal Component Analysis (PCA) improves the quality of the large-scale mobility data and so the estimation results. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

In the recent decades, an increased interest into multiphase machines has developed due to certain beneficial properties they have over the standard three- phase machines. Proper modelling of a multiphase machine allows access to higher degrees of freedom given by the ability to control higher order harmonic frames. This feature is of particular interest in the area of parameter estimation as it may provide an opportunity to implement methods that are not feasible in three- phase machines. Modelling of a real six- phase machine meant for marine application with a functioning control structure is carried out on MATLAB/Simulink; considering nonlinear relations, cross coupling and saturation effects, the model includes the machine’s behavior in the fundamental frame as well as the next two higher order harmonic frames. Stator resistances and permanent magnet flux linkage are estimated online and simultaneously during various operating points. It is shown that stator resistance may be accurately estimated by means of DC test signal injections into the highest order harmonic frame; thus, minimizing torque ripple and additional loss generation. Permanent magnet flux linkage is accurately estimated via reactive power calculations on the fundamental frame which remains undisturbed by the aforementioned test signals. / Under de senaste decennierna har intresset för flerfasmaskiner ökat, på grund av i vissa avseenden, fördelaktiga egenskaper i jämförelse med traditionella trefasmaskiner. En väl genomförd modellering av flerfasmaskiner möjliggör högre grader av frihet givet förmågan att kontrollera frekvensplan av högre ordning. Denna funktion är av särskilt intresse inom parameteruppskattning, då det kan ge möjligheter att implementera metoder som är omöjliga i trefas maskiner. En modell av en verklig sexfasmaskin, byggd för en marin tillämpning och med ett givet styrsystem, har skapats i MATLAB/Simulink. Modellen tar hänsyn till ickelinjära förhållanden, korskopplingar och mättning. Den inkluderar även maskinens egenskaper i det fundamentala frekvensplanet och de två nästkommande frekvensplanen av högre ordning. Statorresistansen och permanentmagneternas sammanlänkade flöden uppskattas i realtid för olika driftsförhållanden. Det visar sig att statorresistansen kan uppskattas noggrant genom inmatning av en DC testsignal i högsta ordningens frekvensplan vilket minimerar momentrippel och extra förluster. Permanentmagneternas flöden kan uppskattas noggrant genom beräkning av den reaktiva effekten i det fundamentala frekvensplanet som är opåverkat av de ovan nämnda testsignalerna.

Les énergies renouvelables, l’énergie sous la forme électrique et son transport à l’aide de réseaux électriques intelligents représentent aujourd’hui des enjeux majeurs car ils ont de grands impacts environnementaux et sociétaux. Ainsi, la production, le transport et la gestion de l’énergie électrique, continuent toujours à susciter un intérêt croissant. Pour atteindre ces objectifs, plusieurs verrous technologiques doivent être levés. Au-delà des questions liées aux architectures des réseaux électriques, aux modèles, aux outils de dimensionnement, à la formalisation de caractéristiques et d’indicateurs, aux contraintes et aux critères, à la gestion et à la production décentralisée, la qualité de la puissance électrique est centrale pour la fiabilité de l’ensemble du système de distribution. Les perturbations affectent la qualité des signaux électriques et peuvent provoquer des conséquences graves sur les autres équipements connectés au réseau. Les travaux de cette thèse s’inscrivent dans ce contexte et de fait ils sont orientés vers le développement de modèles, d’indicateurs et de méthodes de traitement des signaux dédiés à la surveillance en temps-réel des performances des réseaux de distribution électrique.Cette thèse analyse la qualité de la puissance électrique, en prenant en compte plusieurs caractéristiques bien connues ainsi que leur pertinence. Les modèles des systèmes électriques et les méthodes de traitement du signal pour estimer leurs paramètres sont étudiés pour des applications en temps-réel de surveillance, de diagnostic et de contrôle sous diverses conditions. Parmi tous, la fréquence fondamentale est l’un des paramètres les plus importants pour caractériser un système de distribution électrique. En effet, sa valeur qui est censée être une constante, varie en permanence et reflète la dynamique de l’énergie électrique disponible. La fréquence peut également être affectée par certaines productions d’énergie renouvelable et peut être influencée par des mauvaises synchronisations de certains équipements. En outre, la puissance absorbée par les charges ou produite par des sources est généralement différente d’une phase à l’autre. Évidemment, la plupart des installations électriques existantes avec plusieurs phases, qu’elles soient résidentielles ou industrielles, travaillent dans des conditions déséquilibrées. Identifier les composantes symétriques de tension est dans ce cas un moyen pertinent pour quantifier le déséquilibre entre les phases d’un système électrique.De nouvelles représentations de type espace d’état et modélisant des systèmes électriques sont proposées pour estimer la fréquence fondamentale et pour identifier les composantes symétriques de tension des systèmes électriques triphasés et déséquilibrés. Le premier modèle d’espace d’état proposé considère la fréquence fondamentale comme connue ou obtenue par un autre estimateur. En contrepartie, il fournit les autres paramètres caractérisant le système électrique. Un second modèle d’état-espace est introduit. Il est original dans le sens où il ne nécessite aucune connaissance de la fréquence fondamentale. Une de ses variables d’état est directement reliée à la fréquence et permet donc de la déduire. En outre, ce nouvel espace d’état est parfaitement capable de représenter des systèmes électriques à trois phases équilibrés et non équilibrés. [...] / Renewable energy, electricity and smart grids are core subjects as they have great environmental and societal impacts. Thus, generating, transporting and managing electric energy, i.e., power, still continue to drive a growing interest. In order to properly achieve these goals, several locks must be removed. Beyond issues related to the distribution architecture, the formalization of models, sizing tools, features and indicators, constraints and criteria, decentralized generation and energy management, power quality is central for the whole grid’s reliability. Disturbances affect the power quality and can cause serious impact on other equipment connected to the grid. The work of this thesis is part of this context and focuses on the development of models, indicators, and signal processing methods for power quality monitoring in time-varying power distribution systems.This thesis analyzes the power quality including several well-known features and their relevance. Power system models and signal processing methods for estimating their parameters are investigated for the purpose of real-time monitoring, diagnostic and control tasks under various operating conditions. Among all, the fundamental frequency is one of the most important parameters of a power distribution system. Indeed, its value which is supposed to be a constant varies continuously and reflects the dynamic availability of electric power. The fundamental frequency can also be affected by renewable energy generation and by nasty synchronization of some devices. Moreover, the power absorbed by loads or produced by sources is generally different from one phase to the other one. Obviously, most of the existing residential and industrial electrical installations with several phases work under unbalanced conditions. Identifying the symmetrical components is therefore an efficient way to quantify the imbalance between the phases of a grid. New state-space representations of power systems are proposed for estimating the fundamental frequency and for identifying the voltage symmetrical components of unbalanced three-phase power systems. A first state-space representation is developed by supposing the fundamental frequency to be known or to be calculated by another estimator. In return, it provides other parameters and characteristics from the power system. Another original state-space model is introduced which does not require the fundamental frequency. Here, one state variable is a function of the frequency which can thus be deduced. Furthermore this new state-space model is perfectly are able to represent a three-phase power system in both balanced and unbalanced conditions. This not the case of lots of existing models. The advantage of the proposed state-space representation is that it gives directly access to physical parameters of the system, like the frequency and the amplitude and phase values of the voltage symmetrical components. Power systems parameters can thus be estimated in real-time by using the new state-space with an online estimation process like an Extended Kalman Filter (EKF). The digital implementation of the proposed methods presents small computational requirement, elegant recursive properties, and optimal estimations with Gaussian error statistics.The methods have been implemented and validated through various tests respecting real technical constraints and operating conditions. The methods can be integrated in active power filtering schemes or load-frequency control strategies to monitor power systems and to compensate for electrical disturbances.